[update] adding possibility to display other waveform data (e.g. denoised/synthetic) together with genuine data for comparison

closes #24

PyLoT.py

@@ -24,18 +24,16 @@ https://www.iconfinder.com/iconsets/flavour
 """
 
 import argparse
-import matplotlib
+import json
 import os
 import platform
 import shutil
 import sys
-import copy
-
 import traceback
-
-import json
 from datetime import datetime
 
+import matplotlib
+
 matplotlib.use('Qt5Agg')
 
 from PySide2 import QtGui, QtCore, QtWidgets
@@ -44,8 +42,8 @@ from PySide2.QtCore import QCoreApplication, QSettings, Signal, QFile, \
 from PySide2.QtGui import QIcon, QKeySequence, QPixmap, QStandardItem
 from PySide2.QtWidgets import QMainWindow, QInputDialog, QFileDialog, \
     QWidget, QHBoxLayout, QVBoxLayout, QStyle, QLabel, QFrame, QAction, \
-    QDialog, QErrorMessage, QApplication, QMessageBox, QSplashScreen, \
+    QDialog, QApplication, QMessageBox, QSplashScreen, \
-    QActionGroup, QListWidget, QLineEdit, QListView, QAbstractItemView, \
+    QActionGroup, QListWidget, QListView, QAbstractItemView, \
     QTreeView, QComboBox, QTabWidget, QPushButton, QGridLayout, QTableWidgetItem, QTableWidget
 import numpy as np
 from obspy import UTCDateTime, Stream
@@ -60,16 +58,15 @@ try:
     from matplotlib.backends.backend_qt5agg import FigureCanvas
 except ImportError:
     from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
-from matplotlib.backends.backend_qt5agg import NavigationToolbar2QT as NavigationToolbar
 from matplotlib.figure import Figure
 
-from pylot.core.analysis.magnitude import LocalMagnitude, MomentMagnitude
+from pylot.core.analysis.magnitude import LocalMagnitude, MomentMagnitude, calcsourcespec
 from pylot.core.io.data import Data
 from pylot.core.io.inputs import FilterOptions, PylotParameter
 from autoPyLoT import autoPyLoT
 from pylot.core.pick.compare import Comparison
-from pylot.core.pick.utils import symmetrize_error, getQualityFromUncertainty, getPickQuality, get_quality_class
+from pylot.core.pick.utils import getQualityFromUncertainty
-from pylot.core.io.phases import picksdict_from_picks, picks_from_picksdict
+from pylot.core.io.phases import picksdict_from_picks
 import pylot.core.loc.nll as nll
 from pylot.core.util.errors import DatastructureError, \
     OverwriteError
@@ -79,20 +76,21 @@ from pylot.core.util.utils import fnConstructor, getLogin, \
     full_range, readFilterInformation, pick_color_plt, \
     pick_linestyle_plt, identifyPhaseID, excludeQualityClasses, \
     transform_colors_mpl, transform_colors_mpl_str, getAutoFilteroptions, check_all_obspy, \
-    check_all_pylot, get_Bool, get_None, SetChannelComponents
+    check_all_pylot, get_bool, get_None
 from pylot.core.util.gui import make_pen
 from pylot.core.util.event import Event
 from pylot.core.io.location import create_creation_info, create_event
 from pylot.core.util.widgets import FilterOptionsDialog, NewEventDlg, \
     PylotCanvas, WaveformWidgetPG, PropertiesDlg, HelpForm, createAction, PickDlg, \
     ComparisonWidget, TuneAutopicker, PylotParaBox, AutoPickDlg, CanvasWidget, AutoPickWidget, \
-    CompareEventsWidget, ProgressBarWidget, AddMetadataWidget, SingleTextLineDialog, LogWidget
+    CompareEventsWidget, ProgressBarWidget, AddMetadataWidget, SingleTextLineDialog, LogWidget, PickQualitiesFromXml, \
+    SourceSpecWindow, ChooseWaveFormWindow, SpectrogramTab
 from pylot.core.util.array_map import Array_map
 from pylot.core.util.structure import DATASTRUCTURE
 from pylot.core.util.thread import Thread, Worker
 from pylot.core.util.version import get_git_version as _getVersionString
 from pylot.core.io.getEventListFromXML import geteventlistfromxml
-from pylot.core.io.getQualitiesfromxml import getQualitiesfromxml
+from pylot.core.io.phases import getQualitiesfromxml
 
 from pylot.styles import style_settings
 
@@ -119,17 +117,19 @@ class MainWindow(QMainWindow):
         if not infile:
             infile = os.path.join(os.path.expanduser('~'), '.pylot', 'pylot.in')
             print('Using default input file {}'.format(infile))
-        if os.path.isfile(infile) == False:
+        if os.path.isfile(infile) is False:
-            infile = QFileDialog().getOpenFileName(caption='Choose PyLoT-input file')
+            infile = QFileDialog().getOpenFileName(caption='Choose PyLoT-input file')[0]
 
-            if not os.path.exists(infile[0]):
+            if not os.path.exists(infile):
                 QMessageBox.warning(self, "PyLoT Warning",
-                                    "No PyLoT-input file declared!")
+                                    "No PyLoT-input file declared! Using default parameters!")
-                sys.exit(0)
+                infile = None
-            self.infile = infile[0]
+
-        else:
-            self.infile = infile
         self._inputs = PylotParameter(infile)
+        if not infile:
+            self._inputs.reset_defaults()
+
+        self.infile = infile
         self._props = None
 
         self.gain = 1.
@@ -181,6 +181,7 @@ class MainWindow(QMainWindow):
         self.autodata = Data(self)
 
         self.fnames = None
+        self.fnames_comp = None
         self._stime = None
 
         # track deleted picks for logging
@@ -257,7 +258,7 @@ class MainWindow(QMainWindow):
             self._inputs.export2File(infile)
         self.infile = infile
 
-    def setupUi(self):
+    def setupUi(self, use_logwidget=False):
         try:
             self.startTime = min(
                 [tr.stats.starttime for tr in self.data.wfdata])
@@ -436,9 +437,9 @@ class MainWindow(QMainWindow):
                                                  None, paraIcon,
                                                  "Modify Parameter")
         self.deleteAutopicksAction = self.createAction(self, "Delete Autopicks",
-                                                 self.deleteAllAutopicks,
+                                                       self.deleteAllAutopicks,
-                                                 None, deleteIcon,
+                                                       None, deleteIcon,
-                                                 "Delete all automatic picks from Project.")
+                                                       "Delete all automatic picks from Project.")
         self.filterActionP = createAction(parent=self, text='Apply P Filter',
                                           slot=self.filterP,
                                           icon=self.filter_icon_p,
@@ -483,7 +484,7 @@ class MainWindow(QMainWindow):
                                                               "automatic pick "
                                                               "data.", False)
         self.compare_action.setEnabled(False)
-        self.qualities_action = self.createAction(parent=self, text='Show pick qualitites...',
+        self.qualities_action = self.createAction(parent=self, text='Show pick qualities...',
                                                   slot=self.pickQualities, shortcut='Alt+Q',
                                                   icon=qualities_icon, tip='Histogram of pick qualities')
         self.qualities_action.setEnabled(False)
@@ -492,10 +493,10 @@ class MainWindow(QMainWindow):
         self.qualities_action.setVisible(True)
 
         self.eventlist_xml_action = self.createAction(parent=self, text='Create Eventlist from XML',
-                                                  slot=self.eventlistXml, shortcut='Alt+X',
+                                                      slot=self.eventlistXml, shortcut='Alt+X',
-                                                  icon=eventlist_xml_icon, tip='Create an Eventlist from a XML File')
+                                                      icon=eventlist_xml_icon,
+                                                      tip='Create an Eventlist from a XML File')
         self.eventlist_xml_action.setEnabled(False)
-
         printAction = self.createAction(self, "&Print event ...",
                                         self.show_event_information, QKeySequence.Print,
                                         print_icon,
@@ -505,6 +506,10 @@ class MainWindow(QMainWindow):
                                        """Show either the documentation
                                        homepage (internet connection available),
                                        or shipped documentation files.""")
+        logAction = self.createAction(self, "&Show Log", self.showLogWidget,
+                                      tip="""Display Log""")
+
+        logAction.setEnabled(use_logwidget)
 
         # create button group for component selection
 
@@ -559,7 +564,8 @@ class MainWindow(QMainWindow):
                                                                                   ' the complete project on grid engine.')
         self.auto_pick_sge.setEnabled(False)
 
-        pickActions = (self.auto_tune, self.auto_pick, self.compare_action, self.qualities_action, self.eventlist_xml_action)
+        pickActions = (
+            self.auto_tune, self.auto_pick, self.compare_action, self.qualities_action, self.eventlist_xml_action)
 
         # pickToolBar = self.addToolBar("PickTools")
         # pickToolActions = (selectStation, )
@@ -570,7 +576,7 @@ class MainWindow(QMainWindow):
                                                    shortcut='Alt+Ctrl+L',
                                                    icon=locate_icon,
                                                    tip='Locate the event using '
-                                                 'the displayed manual arrivals.')
+                                                       'the displayed manual arrivals.')
         self.locateEventAction.setEnabled(False)
 
         locationToolActions = (self.locateEventAction,)
@@ -600,11 +606,10 @@ class MainWindow(QMainWindow):
         self.autoPickMenu = self.pickMenu.addMenu(self.autopicksicon_small, 'Automatic picking')
         self.autoPickMenu.setEnabled(False)
 
-
         autoPickActions = (self.auto_pick, self.auto_pick_local, self.auto_pick_sge)
 
         self.helpMenu = self.menuBar().addMenu('&Help')
-        helpActions = (helpAction,)
+        helpActions = (helpAction, logAction)
 
         fileToolActions = (self.newProjectAction,
                            self.openProjectAction, self.saveProjectAction,
@@ -695,14 +700,17 @@ class MainWindow(QMainWindow):
         wf_tab = QtWidgets.QWidget(self)
         array_tab = QtWidgets.QWidget(self)
         events_tab = QtWidgets.QWidget(self)
+        spectro_tab = QtWidgets.QWidget(self)
 
         # init main widgets layouts
         self.wf_layout = QtWidgets.QVBoxLayout()
         self.array_layout = QtWidgets.QVBoxLayout()
         self.events_layout = QtWidgets.QVBoxLayout()
+        self.spectro_layout = QtWidgets.QVBoxLayout()
         wf_tab.setLayout(self.wf_layout)
         array_tab.setLayout(self.array_layout)
         events_tab.setLayout(self.events_layout)
+        spectro_tab.setLayout(self.spectro_layout)
 
         # tighten up layouts inside tabs
         for layout in [self.wf_layout, self.array_layout, self.events_layout]:
@@ -713,12 +721,14 @@ class MainWindow(QMainWindow):
         self.tabs.addTab(wf_tab, 'Waveform Plot')
         self.tabs.addTab(array_tab, 'Array Map')
         self.tabs.addTab(events_tab, 'Eventlist')
+        self.tabs.addTab(spectro_tab, 'Spectro')
 
         self.wf_layout.addWidget(self.no_data_label)
         self.wf_layout.addWidget(self.wf_scroll_area)
         self.wf_scroll_area.setWidgetResizable(True)
         self.init_array_tab()
         self.init_event_table()
+        self.init_spectro_tab()
         self.tabs.setCurrentIndex(0)
 
         self.eventLabel = QLabel()
@@ -731,13 +741,19 @@ class MainWindow(QMainWindow):
         _widget.setLayout(self._main_layout)
         _widget.showFullScreen()
 
-        self.logwidget = LogWidget(parent=None)
+
-        self.logwidget.show()
+        if use_logwidget:
-        sys.stdout = self.logwidget.stdout
+            self.logwidget = LogWidget(parent=None)
-        sys.stderr = self.logwidget.stderr
+            self.logwidget.show()
+            self.stdout = self.logwidget.stdout
+            self.stderr = self.logwidget.stderr
 
         self.setCentralWidget(_widget)
 
+        # Need to store PickQualities Window somewhere so it doesnt disappear
+        self.pickQualitiesWindow = None
+
+
     def init_wfWidget(self):
         xlab = self.startTime.strftime('seconds since %Y/%m/%d %H:%M:%S (%Z)')
         plottitle = None  # "Overview: {0} components ".format(self.getComponent())
@@ -771,6 +787,10 @@ class MainWindow(QMainWindow):
         self.ref_event_button.setEnabled(False)
         self.test_event_button.setEnabled(False)
 
+    def showLogWidget(self):
+        self.logwidget.show()
+        self.logwidget.activateWindow()
+
     def keyPressEvent(self, event):
         if event.key() == QtCore.Qt.Key.Key_Control:
             self._ctrl = True
@@ -802,7 +822,7 @@ class MainWindow(QMainWindow):
     def modify_gain(self, direction, factor):
         assert (direction in ['+', '-']), 'unknown direction'
         if self._ctrl:
-            factor = factor**3
+            factor = factor ** 3
         if direction == '+':
             self.gain *= factor
         elif direction == '-':
@@ -954,7 +974,6 @@ class MainWindow(QMainWindow):
 
             self.recentProjectsMenu.addAction(action)
 
-
     @property
     def inputs(self):
         return self._inputs
@@ -992,7 +1011,7 @@ class MainWindow(QMainWindow):
         if not sld.exec_():
             return
         fext = sld.lineEdit.text()
-        #fext = '.xml'
+        # fext = '.xml'
         for event in events:
             path = event.path
             eventname = path.split('/')[-1]  # or event.pylot_id
@@ -1024,7 +1043,7 @@ class MainWindow(QMainWindow):
             data_new = Data(self, evtdata=str(fname))
             # MP MP commented because adding several picks might cause inconsistencies
             data = data_new
-            #data += data_new
+            # data += data_new
         except ValueError:
             qmb = QMessageBox(self, icon=QMessageBox.Question,
                               text='Warning: Missmatch in event identifiers {} and {}. Continue?'.format(
@@ -1112,16 +1131,19 @@ class MainWindow(QMainWindow):
             else:
                 return
 
-    def getWFFnames_from_eventbox(self, eventbox=None):
+    def getWFFnames_from_eventbox(self, eventbox: str = None, subpath: str = None) -> list:
         '''
         Return waveform filenames from event in eventbox.
         '''
         # TODO: add dataStructure class for obspyDMT here, this is just a workaround!
         eventpath = self.get_current_event_path(eventbox)
-        basepath = eventpath.split(os.path.basename(eventpath))[0]
+        if subpath:
+            eventpath = os.path.join(eventpath, subpath)
+        if not os.path.isdir(eventpath):
+            return []
         if self.dataStructure:
             if not eventpath:
-                return
+                return []
             fnames = [os.path.join(eventpath, f) for f in os.listdir(eventpath)]
         else:
             raise DatastructureError('not specified')
@@ -1165,7 +1187,7 @@ class MainWindow(QMainWindow):
         '''
         if not self.project:
             self.createNewProject()
-        ed = getExistingDirectories(self, 'Select event directories...')
+        ed = GetExistingDirectories(self, 'Select event directories...')
         if ed.exec_():
             eventlist = [event for event in ed.selectedFiles() if not event.endswith('EVENTS-INFO')]
             basepath = eventlist[0].split(os.path.basename(eventlist[0]))[0]
@@ -1173,7 +1195,8 @@ class MainWindow(QMainWindow):
             eventlist_file = os.path.join(basepath, 'eventlist.txt')
             if os.path.isfile(eventlist_file):
                 with open(eventlist_file, 'r') as infile:
-                    eventlist_subset = [os.path.join(basepath, filename.split('\n')[0]) for filename in infile.readlines()]
+                    eventlist_subset = [os.path.join(basepath, filename.split('\n')[0]) for filename in
+                                        infile.readlines()]
                     msg = 'Found file "eventlist.txt" in database path. WILL ONLY USE SELECTED EVENTS out of {} events ' \
                           'contained in this subset'
                     print(msg.format(len(eventlist_subset)))
@@ -1224,7 +1247,7 @@ class MainWindow(QMainWindow):
             print('Warning: Could not automatically init folder structure. ({})'.format(e))
 
         settings = QSettings()
-        settings.setValue("data/dataRoot", dirs['datapath'])  #d irs['rootpath'])
+        settings.setValue("data/dataRoot", dirs['datapath'])  # d irs['rootpath'])
         settings.sync()
 
         if not self.project.eventlist:
@@ -1365,7 +1388,7 @@ class MainWindow(QMainWindow):
         index = eventBox.currentIndex()
         tv = QtWidgets.QTableView()
         header = tv.horizontalHeader()
-        header.setResizeMode(QtWidgets.QHeaderView.ResizeToContents)
+        header.setSectionResizeMode(QtWidgets.QHeaderView.ResizeToContents)
         header.setStretchLastSection(True)
         header.hide()
         tv.verticalHeader().hide()
@@ -1538,6 +1561,7 @@ class MainWindow(QMainWindow):
             event = self.get_current_event()
         if not type(outformats) == list:
             outformats = [outformats]
+
         def getSavePath(event, directory, outformats):
             if not directory:
                 title = 'Save event data as {} to directory ...'.format(outformats)
@@ -1660,16 +1684,55 @@ class MainWindow(QMainWindow):
         self.cmpw.show()
 
     def pickQualities(self):
-        path = self._inputs['rootpath'] + '/'  + self._inputs['datapath'] + '/' + self._inputs['database']
+        path = self.get_current_event_path()
-        getQualitiesfromxml(path)
+        (_, plot) = getQualitiesfromxml(path, self._inputs.get('timeerrorsP'), self._inputs.get('timeerrorsS'),plotflag=1)
+        self.pickQualitiesWindow = PickQualitiesFromXml(figure=plot, path=self.get_current_event_path(),inputVar=self._inputs)
+        self.pickQualitiesWindow.showUI()
         return 
 
+    # WIP JG
     def eventlistXml(self):
-        path = self._inputs['rootpath'] + '/'  + self._inputs['datapath'] + '/' + self._inputs['database']
+        path = self._inputs['rootpath'] + '/' + self._inputs['datapath'] + '/' + self._inputs['database']
         outpath = self.project.location[:self.project.location.rfind('/')]
         geteventlistfromxml(path, outpath)
         return
 
+        # WIP JG
+    def spectogramView(self):
+        global test
+        stations = []
+        names = []
+        traces = {}
+        for tr in self.get_data().wfdata.traces:
+            if not tr.stats.station in stations:
+                stations.append(tr.stats.station)
+                names.append(tr.stats.network + '.' + tr.stats.station)
+        for station in stations:
+            traces[station] = {}
+        for ch in ['Z', 'N', 'E']:
+            for tr in self.get_data().wfdata.select(component=ch).traces:
+                traces[tr.stats.station][ch] = tr
+
+        names.sort()
+        a = self.get_current_event()
+
+        print (self.get_data().wfdata.traces[0])
+
+        test = SpectrogramTab(traces, self.get_data().wfdata)
+        height = self.tabs.widget(0).height()
+        width = self.tabs.widget(0).width()
+        self.tabs.setCurrentIndex(3)
+        figCanvas = test.makeSpecFig(direction=self.dispComponent, height = height, width = width, parent = self.tabs.widget)
+        return figCanvas
+        #self.spectro_layout.addWidget()
+        # self.get_data().wfdata.spectrogram()
+        # self.tabs.addTab(figCanvas, 'Spectrogram')
+        # self.tabs[3] = figCanvas
+        # self.refreshTabs()
+        # test.show()
+
+
+
     def compareMulti(self):
         if not self.compareoptions:
             return
@@ -1855,6 +1918,13 @@ class MainWindow(QMainWindow):
                     self.newWF(plot=False)
                 self.update_obspy_dmt()
                 self.refresh_array_map()
+        if self.tabs.currentIndex() == 3:
+            if self.spectroWidget != None:
+                self.spectro_layout.removeWidget(self.spectroWidget)
+            newSpectroWidget = self.spectogramView()
+            self.spectro_layout.addWidget(newSpectroWidget)
+            self.spectroWidget = newSpectroWidget
+
 
     def newWF(self, event=None, plot=True):
         '''
@@ -1885,13 +1955,20 @@ class MainWindow(QMainWindow):
 
     def prepareLoadWaveformData(self):
         self.fnames = self.getWFFnames_from_eventbox()
-        self.fnames_syn = []
+        self.fnames_comp = []
+        fnames_comp = self.getWFFnames_from_eventbox(subpath='compare')
+        self.dataPlot.activateCompareOptions(bool(fnames_comp))
+        if fnames_comp:
+            if self.dataPlot.comp_checkbox.isChecked():
+                self.fnames_comp = fnames_comp
+
         eventpath = self.get_current_event_path()
         basepath = eventpath.split(os.path.basename(eventpath))[0]
         self.obspy_dmt = check_obspydmt_structure(basepath)
         self.dataPlot.activateObspyDMToptions(self.obspy_dmt)
         if self.obspy_dmt:
             self.prepareObspyDMT_data(eventpath)
+            self.dataPlot.activateCompareOptions(True)
 
     def loadWaveformData(self):
         '''
@@ -1922,15 +1999,15 @@ class MainWindow(QMainWindow):
             tstop = None
 
         self.data.setWFData(self.fnames,
-                            self.fnames_syn,
+                            self.fnames_comp,
                             checkRotated=True,
                             metadata=self.metadata,
                             tstart=tstart,
-                            tstop=tstop,)
+                            tstop=tstop, )
 
     def prepareObspyDMT_data(self, eventpath):
         qcbox_processed = self.dataPlot.qcombo_processed
-        qcheckb_syn = self.dataPlot.syn_checkbox
+        qcheckb_syn = self.dataPlot.comp_checkbox
         qcbox_processed.setEnabled(False)
         qcheckb_syn.setEnabled(False)
         for fpath in os.listdir(eventpath):
@@ -1938,8 +2015,8 @@ class MainWindow(QMainWindow):
             if 'syngine' in fpath:
                 eventpath_syn = os.path.join(eventpath, fpath)
                 qcheckb_syn.setEnabled(True)
-                if self.dataPlot.syn_checkbox.isChecked():
+                if self.dataPlot.comp_checkbox.isChecked():
-                    self.fnames_syn = [os.path.join(eventpath_syn, filename) for filename in os.listdir(eventpath_syn)]
+                    self.fnames_comp = [os.path.join(eventpath_syn, filename) for filename in os.listdir(eventpath_syn)]
             if 'processed' in fpath:
                 qcbox_processed.setEnabled(True)
         if qcbox_processed.isEnabled():
@@ -2112,6 +2189,7 @@ class MainWindow(QMainWindow):
             self.locateEventAction.setEnabled(True)
             self.qualities_action.setEnabled(True)
             self.eventlist_xml_action.setEnabled(True)
+
         if True in self.comparable.values():
             self.compare_action.setEnabled(True)
         self.draw()
@@ -2123,7 +2201,7 @@ class MainWindow(QMainWindow):
         if self.obspy_dmt:
             invpath = os.path.join(self.get_current_event_path(), 'resp')
             if not invpath in self.metadata.inventories:
-                self.metadata.add_inventory(invpath, obspy_dmt_inv = True)
+                self.metadata.add_inventory(invpath, obspy_dmt_inv=True)
             # check if directory is empty
             if os.listdir(invpath):
                 self.init_map_button.setEnabled(True)
@@ -2219,7 +2297,7 @@ class MainWindow(QMainWindow):
         comp = self.getComponent()
         title = 'section: {0} components'.format(zne_text[comp])
         wfst = self.get_data().getWFData()
-        wfsyn = self.get_data().getSynWFData()
+        wfsyn = self.get_data().getAltWFdata()
         if self.filterActionP.isChecked() and filter:
             self.filterWaveformData(plot=False, phase='P')
         elif self.filterActionS.isChecked() and filter:
@@ -2228,7 +2306,7 @@ class MainWindow(QMainWindow):
         # wfst += self.get_data().getWFData().select(component=alter_comp)
         plotWidget = self.getPlotWidget()
         self.adjustPlotHeight()
-        if get_Bool(settings.value('large_dataset')) == True:
+        if get_bool(settings.value('large_dataset')):
             self.plot_method = 'fast'
         else:
             self.plot_method = 'normal'
@@ -2531,17 +2609,21 @@ class MainWindow(QMainWindow):
             print("Warning! No network, station, and location info available!")
             return
         self.update_status('picking on station {0}'.format(station))
-        data = self.get_data().getOriginalWFData().copy()
+        wfdata = self.get_data().getOriginalWFData().copy()
+        wfdata_comp = self.get_data().getAltWFdata().copy()
         event = self.get_current_event()
         wftype = self.dataPlot.qcombo_processed.currentText() if self.obspy_dmt else None
         pickDlg = PickDlg(self, parameter=self._inputs,
-                          data=data.select(station=station),
+                          data=wfdata.select(station=station),
+                          data_compare=wfdata_comp.select(station=station),
                           station=station, network=network,
                           location=location,
                           picks=self.getPicksOnStation(station, 'manual'),
                           autopicks=self.getPicksOnStation(station, 'auto'),
                           metadata=self.metadata, event=event,
-                          filteroptions=self.filteroptions, wftype=wftype)
+                          model=self.inputs.get('taup_model'),
+                          filteroptions=self.filteroptions, wftype=wftype,
+                          show_comp_data=self.dataPlot.comp_checkbox.isChecked())
         if self.filterActionP.isChecked():
             pickDlg.currentPhase = "P"
             pickDlg.filterWFData()
@@ -2654,7 +2736,7 @@ class MainWindow(QMainWindow):
         self.init_fig_dict()
         # if not self.tap:
         # init TuneAutopicker object
-        self.tap = TuneAutopicker(self)
+        self.tap = TuneAutopicker(self, self.obspy_dmt)
         # first call of update to init tabs with empty canvas
         self.update_autopicker()
         # connect update signal of TuneAutopicker with update function
@@ -2696,9 +2778,9 @@ class MainWindow(QMainWindow):
             # init event selection options for autopick
             self.pickoptions = [('current event', self.get_current_event, None),
                                 ('tune events', self.get_ref_events, self._style['ref']['rgba']),
-                                ('test events', self.get_test_events, self._style['test']['rgba']),]
+                                ('test events', self.get_test_events, self._style['test']['rgba']), ]
-                                #('all (picked) events', self.get_manu_picked_events, None),
+            # ('all (picked) events', self.get_manu_picked_events, None),
-                                #('all events', self.get_all_events, None)]
+            # ('all events', self.get_all_events, None)]
 
             self.listWidget = QListWidget()
             self.setDirty(True)
@@ -2853,7 +2935,9 @@ class MainWindow(QMainWindow):
         self.log_deleted_picks([deleted_pick])
 
     def log_deleted_picks(self, deleted_picks, event_path=None):
-        ''' Log deleted picks to list self.deleted_picks '''
+        '''
+        Log deleted picks to list self.deleted_picks
+        '''
         if not event_path:
             event_path = self.get_current_event_path()
         for deleted_pick in deleted_picks:
@@ -2867,7 +2951,9 @@ class MainWindow(QMainWindow):
             self.deleted_picks[event_path].append(deleted_pick)
 
     def dump_deleted_picks(self, event_path):
-        ''' Save deleted picks to json file for event in event_path. Load old file before and merge'''
+        '''
+        Save deleted picks to json file for event in event_path. Load old file before and merge
+        '''
         try:
             deleted_picks_from_file = self.load_deleted_picks(event_path)
         except Exception as e:
@@ -2896,7 +2982,7 @@ class MainWindow(QMainWindow):
 
     def safetyCopy(self, event_path):
         fpath = self.get_deleted_picks_fpath(event_path)
-        fpath_new =  fpath.split('.json')[0] + '_copy_{}.json'.format(datetime.now()).replace(' ', '_')
+        fpath_new = fpath.split('.json')[0] + '_copy_{}.json'.format(datetime.now()).replace(' ', '_')
         shutil.move(fpath, fpath_new)
 
     def load_deleted_picks(self, event_path):
@@ -3063,11 +3149,13 @@ class MainWindow(QMainWindow):
         lt = locateTool[loctool]
         # get working directory
         locroot = parameter['nllocroot']
+        #locroot = 'E:/NLL/src/Insheim'
         if locroot is None:
             self.PyLoTprefs()
             self.locate_event()
 
         ctrfile = os.path.join(locroot, 'run', parameter['ctrfile'])
+        #ctrfile = 'E:/NLL/src/Insheim/run/Insheim_min1d032016.in'
         ttt = parameter['ttpatter']
         outfile = parameter['outpatter']
         eventname = self.get_current_event_name()
@@ -3078,7 +3166,7 @@ class MainWindow(QMainWindow):
         phasefile = os.path.join(obsdir, filename + '.obs')
         lt.modify_inputs(ctrfile, locroot, filename, phasefile, ttt)
         try:
-            lt.locate(ctrfile)
+            lt.locate(ctrfile, self._inputs)
         except RuntimeError as e:
             print(e.message)
         # finally:
@@ -3130,9 +3218,9 @@ class MainWindow(QMainWindow):
         Try to init array map widget. If no metadata are given,
         self.get_metadata will be called.
         '''
-        if checked: pass # dummy argument for QAction trigger signal
+        if checked: pass  # dummy argument for QAction trigger signal
         self.tabs.setCurrentIndex(1)
-        # if there is no metadata (invetories is an empty list), just initialize the default empty tab
+        # if there is no metadata (inventories is an empty list), just initialize the default empty tab
         if not self.metadata.inventories:
             self.init_array_tab()
             return
@@ -3154,6 +3242,15 @@ class MainWindow(QMainWindow):
         self.tabs.setCurrentIndex(index)
         self.refresh_array_map()
 
+    def init_spectro_tab(self):
+        '''
+        Init spectrogram tab with currently selected event.
+        '''
+        self.spectroWidget = None
+        #self.spectro_layout.addWidget( self.spectogramView() )
+        pass
+
+
     def array_map_thread(self):
         '''
         Start modal thread to init the array_map object.
@@ -3293,9 +3390,9 @@ class MainWindow(QMainWindow):
             item_depth = QTableWidgetItem()
             item_momentmag = QTableWidgetItem()
             item_localmag = QTableWidgetItem()
-            item_nmp = QTableWidgetItem('{}'.format(ma_count['manual']))#, ma_count_total['manual']))
+            item_nmp = QTableWidgetItem('{}'.format(ma_count['manual']))  # , ma_count_total['manual']))
             item_nmp.setIcon(self.manupicksicon_small)
-            item_nap = QTableWidgetItem('{}'.format(ma_count['auto']))#, ma_count_total['auto']))
+            item_nap = QTableWidgetItem('{}'.format(ma_count['auto']))  # , ma_count_total['auto']))
             item_nap.setIcon(self.autopicksicon_small)
             item_ref = QTableWidgetItem()
             item_test = QTableWidgetItem()
@@ -3376,7 +3473,7 @@ class MainWindow(QMainWindow):
                     self.event_table.setCellWidget(r_index, c_index, item)
 
         header = self.event_table.horizontalHeader()
-        header.setResizeMode(QtWidgets.QHeaderView.ResizeToContents)
+        header.setSectionResizeMode(QtWidgets.QHeaderView.ResizeToContents)
         header.setStretchLastSection(True)
         self.event_table.cellChanged[int, int].connect(cell_changed)
         self.event_table.cellClicked[int, int].connect(cell_clicked)
@@ -3422,7 +3519,7 @@ class MainWindow(QMainWindow):
                 event, time, lat, lon, depth, ml, mw, nmp, nap, tune, test, notes = row
                 row_str = ''
                 for index in range(len(row)):
-                    row_str += '{}'+'{}'.format(separator)
+                    row_str += '{}' + '{}'.format(separator)
 
                 row_str = row_str.format(event.text(), time.text(), lat.text(), lon.text(), depth.text(), ml.text(),
                                          mw.text(), nmp.text(), nap.text(), bool(tune.checkState()),
@@ -3451,7 +3548,6 @@ class MainWindow(QMainWindow):
         if event == current_event:
             set_background_color(item_list, QtGui.QColor(*(0, 143, 143, 255)))
 
-
     def set_metadata(self):
         self.project.inventories = self.metadata.inventories
         if self.metadata.inventories:
@@ -3463,7 +3559,7 @@ class MainWindow(QMainWindow):
             self.init_map_button.setEnabled(False)
             self.initMapAction.setEnabled(False)
             self.inventory_label.setText("No inventory set...")
-            #self.setDirty(False)
+            # self.setDirty(False)
 
     def add_metadata(self):
         self.add_metadata_widget = AddMetadataWidget(self, metadata=self.metadata)
@@ -3484,7 +3580,7 @@ class MainWindow(QMainWindow):
 
         wf_select = Stream()
         # restitute only picked traces
-        for station in np.unique(self.getPicks('manual').keys() + self.getPicks('auto').keys()):
+        for station in np.unique(list(self.getPicks('manual').keys()) + list(self.getPicks('auto').keys())):
             wf_select += wf_copy.select(station=station)
 
         corr_wf = restitute_data(wf_select, self.metadata)
@@ -3663,7 +3759,7 @@ class MainWindow(QMainWindow):
             filename = fnm[0] + '.plp'
         self.project.parameter = self._inputs
         settings = QSettings()
-        autosaveXML = get_Bool(settings.value('autosaveXML', True))
+        autosaveXML = get_bool(settings.value('autosaveXML', True))
         if autosaveXML:
             self.exportEvents()
         if not self.project.save(filename): return False
@@ -3687,7 +3783,7 @@ class MainWindow(QMainWindow):
                 self.metadata.clear_inventory()
                 self.project.parameter = self._inputs
                 settings = QSettings()
-                autosaveXML = get_Bool(settings.value('autosaveXML', True))
+                autosaveXML = get_bool(settings.value('autosaveXML', True))
                 if autosaveXML:
                     self.exportEvents()
                 if not self.project.save(): return False
@@ -3711,7 +3807,7 @@ class MainWindow(QMainWindow):
         else:
             self.dataPlot.setPermText(1)
         self.dataPlot.setPermText(0, '| Number of traces: {} | Gain: {}'.format(len(self.getPlotWidget().getPlotDict()),
-                                  self.gain))
+                                                                                self.gain))
 
     def _setDirty(self):
         self.setDirty(True)
@@ -3725,6 +3821,7 @@ class MainWindow(QMainWindow):
 
     def closeEvent(self, event):
         if self.okToContinue():
+            self.logwidget.close()
             event.accept()
         else:
             event.ignore()
@@ -3732,7 +3829,7 @@ class MainWindow(QMainWindow):
             # QMainWindow.closeEvent(self, event)
 
     def setParameter(self, checked=0, show=True):
-        if checked: pass # dummy argument to receive trigger signal (checked) if called by QAction
+        if checked: pass  # dummy argument to receive trigger signal (checked) if called by QAction
         if not self.paraBox:
             self.paraBox = PylotParaBox(self._inputs, parent=self, windowflag=Qt.Window)
             self.paraBox.accepted.connect(self._setDirty)
@@ -3757,7 +3854,6 @@ class MainWindow(QMainWindow):
         self.plotWaveformDataThread()
         self.refreshTabs()
 
-
     def PyLoTprefs(self):
         if not self._props:
             self._props = PropertiesDlg(self, infile=self.infile,
@@ -3776,7 +3872,7 @@ class MainWindow(QMainWindow):
     def helpHelp(self):
         if checkurl():
             form = HelpForm(self,
-                            'https://ariadne.geophysik.ruhr-uni-bochum.de/trac/PyLoT/wiki')
+                            'https://github.com/seismology-RUB/PyLoT')
         else:
             form = HelpForm(self, ':/help.html')
         form.show()
@@ -3786,6 +3882,7 @@ class Project(object):
     '''
     Pickable class containing information of a PyLoT project, like event lists and file locations.
     '''
+
     # TODO: remove rootpath
     def __init__(self):
         self.eventlist = []
@@ -3927,18 +4024,18 @@ class Project(object):
         else:
             filename = self.location
 
-        table = self._table # MP: see below
+        table = self._table  # MP: see below
         try:
             outfile = open(filename, 'wb')
-            self._table = [] # MP: Workaround as long as table cannot be saved as part of project
+            self._table = []  # MP: Workaround as long as table cannot be saved as part of project
             pickle.dump(self, outfile, protocol=pickle.HIGHEST_PROTOCOL)
             self.setDirty(False)
-            self._table = table # MP: see above
+            self._table = table  # MP: see above
             return True
         except Exception as e:
             print('Could not pickle PyLoT project. Reason: {}'.format(e))
             self.setDirty()
-            self._table = table # MP: see above
+            self._table = table  # MP: see above
             return False
 
     @staticmethod
@@ -3955,13 +4052,13 @@ class Project(object):
         return project
 
 
-class getExistingDirectories(QFileDialog):
+class GetExistingDirectories(QFileDialog):
     '''
     File dialog with possibility to select multiple folders.
     '''
 
     def __init__(self, *args):
-        super(getExistingDirectories, self).__init__(*args)
+        super(GetExistingDirectories, self).__init__(*args)
         self.setOption(self.DontUseNativeDialog, True)
         self.setOption(self.ReadOnly, True)
         self.setFileMode(self.Directory)
@@ -3987,16 +4084,7 @@ def create_window():
     return app, app_created
 
 
-def main(args=None):
+def main(project_filename=None, pylot_infile=None, reset_qsettings=False):
-    project_filename = None
-    # args.project_filename = 'C:/Shared/AlpArray/alparray_data/project_alparray_test.plp'
-    pylot_infile = None
-    if args:
-        if args.project_filename:
-            project_filename = args.project_filename
-        if args.input_filename:
-            pylot_infile = args.input_filename
-        reset_qsettings = args.reset_qsettings
 
     # create the Qt application
     pylot_app, app_created = create_window()
@@ -4045,4 +4133,5 @@ if __name__ == "__main__":
     parser.add_argument('--reset_qsettings', default=False, action='store_true',
                         help='reset qsettings (debug option)')
     args = parser.parse_args()
-    sys.exit(main(args))
+    sys.exit(main(project_filename=args.project_filename, pylot_infile=args.input_filename,
+                  reset_qsettings=args.reset_qsettings))

README.md

@@ -1,36 +1,54 @@
 # PyLoT
 
-version: 0.2
+version: 0.3
 
 The Python picking and Localisation Tool
 
-This python library contains a graphical user interfaces for picking
+This python library contains a graphical user interfaces for picking seismic phases. This software needs [ObsPy][ObsPy]
-seismic phases. This software needs [ObsPy][ObsPy]
+and the PySide2 Qt5 bindings for python to be installed first.
-and the PySide Qt4 bindings for python to be installed first.
 
-PILOT has originally been developed in Mathworks' MatLab. In order to
+PILOT has originally been developed in Mathworks' MatLab. In order to distribute PILOT without facing portability
-distribute PILOT without facing portability problems, it has been decided
+problems, it has been decided to redevelop the software package in Python. The great work of the ObsPy group allows easy
-to redevelop the software package in Python. The great work of the ObsPy
+handling of a bunch of seismic data and PyLoT will benefit a lot compared to the former MatLab version.
-group allows easy handling of a bunch of seismic data and PyLoT will
-benefit a lot compared to the former MatLab version.
 
 The development of PyLoT is part of the joint research project MAGS2 and AlpArray.
 
 ## Installation
 
-At the moment there is no automatic installation procedure available for PyLoT.
+At the moment there is no automatic installation procedure available for PyLoT. Best way to install is to clone the
-Best way to install is to clone the repository and add the path to your Python path.
+repository and add the path to your Python path.
+
+It is highly recommended to use Anaconda for a simple creation of a Python installation using either the *pylot.yml* or the *requirements.txt* file found in the PyLoT root directory. First make sure that the *conda-forge* channel is available in your Anaconda installation:
+
+    conda config --add channels conda-forge
+
+Afterwards run (from the PyLoT main directory where the files *requirements.txt* and *pylot.yml* are located)
+
+    conda env create -f pylot.yml
+or
+    
+    conda create --name pylot_38 --file requirements.txt
+
+to create a new Anaconda environment called "pylot_38".
+
+Afterwards activate the environment by typing
+
+    conda activate pylot_38
 
 #### Prerequisites:
 
 In order to run PyLoT you need to install:
 
-- python 2 or 3
+- Python 3
+- obspy
+- pyside2
+- pyqtgraph
+- cartopy
+
+(the following are already dependencies of the above packages):
 - scipy
 - numpy
-- matplotlib
+- matplotlib <= 3.3.x
-- obspy
-- pyside
 
 #### Some handwork:
 
@@ -53,7 +71,8 @@ In the next step you have to copy some files to this directory:
 
     cp path-to-pylot/inputs/pylot_global.in ~/.pylot/pylot.in
 
-and some extra information on error estimates (just needed for reading old PILOT data) and the Richter magnitude scaling relation
+and some extra information on error estimates (just needed for reading old PILOT data) and the Richter magnitude scaling
+relation
 
     cp path-to-pylot/inputs/PILOT_TimeErrors.in path-to-pylot/inputs/richter_scaling.data ~/.pylot/
 
@@ -61,7 +80,6 @@ You may need to do some modifications to these files. Especially folder names sh
 
 PyLoT has been tested on Mac OSX (10.11), Debian Linux 8 and on Windows 10.
 
-
 ## Release notes
 
 #### Features:
@@ -77,20 +95,17 @@ PyLoT has been tested on Mac OSX (10.11), Debian Linux 8 and on Windows 10.
 
 #### Known issues:
 
-- Sometimes an error might occur when using Qt
-
 We hope to solve these with the next release.
 
 ## Staff
 
-Original author(s): L. Kueperkoch, S. Wehling-Benatelli, M. Bischoff (PILOT)
+Original author(s): M. Rische, S. Wehling-Benatelli, L. Kueperkoch, M. Bischoff (PILOT)
 
-Developer(s): S. Wehling-Benatelli, L. Kueperkoch, K. Olbert, M. Bischoff,
+Developer(s): S. Wehling-Benatelli, M. Paffrath, L. Kueperkoch, K. Olbert, M. Bischoff, C. Wollin, M. Rische, D. Arnold, K. Cökerim, S. Zimmermann
-              C. Wollin, M. Rische, M. Paffrath
 
 Others: A. Bruestle, T. Meier, W. Friederich
 
 
 [ObsPy]: http://github.com/obspy/obspy/wiki
 
-September 2017
+April 2022

autoPyLoT.py

@@ -8,6 +8,7 @@ import datetime
 import glob
 import os
 import traceback
+
 from obspy import read_events
 from obspy.core.event import ResourceIdentifier
 
@@ -277,7 +278,7 @@ def autoPyLoT(input_dict=None, parameter=None, inputfile=None, fnames=None, even
                 if not wfdat:
                     print('Could not find station {}. STOP!'.format(station))
                     return
-            #wfdat = remove_underscores(wfdat)
+            # wfdat = remove_underscores(wfdat)
             # trim components for each station to avoid problems with different trace starttimes for one station
             wfdat = check4gapsAndRemove(wfdat)
             wfdat = check4doubled(wfdat)

autopylot.sh

@@ -7,4 +7,6 @@
 #$ -l h_vmem=2G
 #$ -l os=*stretch
 
-python ./autoPyLoT.py -i /home/marcel/.pylot/pylot_alparray_mantle_corr_stack_0.03-0.5.in -dmt processed -c $NSLOTS
+conda activate pylot_38
+
+python ./autoPyLoT.py -i /home/marcel/.pylot/pylot_janis_noisy.in -c $NSLOTS

docs/gui.md

@@ -2,36 +2,36 @@
 
 - [PyLoT Documentation](#pylot-documentation)
 - [PyLoT GUI](#pylot-gui)
-  - [First start](#first-start)
+    - [First start](#first-start)
-  - [Main Screen](#main-screen)
+    - [Main Screen](#main-screen)
-    - [Waveform Plot](#waveform-plot)
+        - [Waveform Plot](#waveform-plot)
-      - [Mouse view controls](#mouse-view-controls)
+            - [Mouse view controls](#mouse-view-controls)
-      - [Buttons](#buttons)
+            - [Buttons](#buttons)
-    - [Array Map](#array-map)
+        - [Array Map](#array-map)
-    - [Eventlist](#eventlist)
+        - [Eventlist](#eventlist)
-  - [Usage](#usage)
+    - [Usage](#usage)
-    - [Projects and Events](#projects-and-events)
+        - [Projects and Events](#projects-and-events)
-    - [Event folder structure](#event-folder-structure)
+        - [Event folder structure](#event-folder-structure)
-    - [Loading event information from CSV file](#loading-event-information-from-csv-file)
+        - [Loading event information from CSV file](#loading-event-information-from-csv-file)
-    - [Adding events to project](#adding-events-to-project)
+        - [Adding events to project](#adding-events-to-project)
-    - [Saving projects](#saving-projects)
+        - [Saving projects](#saving-projects)
-    - [Adding metadata](#adding-metadata)
+        - [Adding metadata](#adding-metadata)
 - [Picking](#picking)
-  - [Manual Picking](#manual-picking)
+    - [Manual Picking](#manual-picking)
-    - [Picking window](#picking-window)
+        - [Picking window](#picking-window)
-      - [Picking Window Settings](#picking-window-settings)
+            - [Picking Window Settings](#picking-window-settings)
-    - [Filtering](#filtering)
+        - [Filtering](#filtering)
-    - [Export and Import of manual picks](#export-and-import-of-manual-picks)
+        - [Export and Import of manual picks](#export-and-import-of-manual-picks)
-      - [Export](#export)
+            - [Export](#export)
-      - [Import](#import)
+            - [Import](#import)
-  - [Automatic Picking](#automatic-picking)
+    - [Automatic Picking](#automatic-picking)
-    - [Tuning](#tuning)
+        - [Tuning](#tuning)
-    - [Production run of the autopicker](#production-run-of-the-autopicker)
+        - [Production run of the autopicker](#production-run-of-the-autopicker)
-    - [Evaluation of automatic picks](#evaluation-of-automatic-picks)
+        - [Evaluation of automatic picks](#evaluation-of-automatic-picks)
-      - [1. Jackknife check](#1-jackknife-check)
+            - [1. Jackknife check](#1-jackknife-check)
-      - [2. Wadati check](#2-wadati-check)
+            - [2. Wadati check](#2-wadati-check)
-    - [Comparison between automatic and manual picks](#comparison-between-automatic-and-manual-picks)
+        - [Comparison between automatic and manual picks](#comparison-between-automatic-and-manual-picks)
-    - [Export and Import of automatic picks](#export-and-import-of-automatic-picks)
+        - [Export and Import of automatic picks](#export-and-import-of-automatic-picks)
 - [Location determination](#location-determination)
 - [FAQ](#faq)
 
@@ -44,15 +44,17 @@ This section describes how to use PyLoT graphically to view waveforms and create
 After opening PyLoT for the first time, the setup routine asks for the following information:
 
 Questions:
+
 1. Full Name
 2. Authority: Enter authority/institution name
 3. Format: Enter output format (*.xml, *.cnv, *.obs)
 
- [//]: <> (TODO: explain what these things mean, where they are used)
+[//]: <> (TODO: explain what these things mean, where they are used)
 
 ## Main Screen
 
-After entering the [information](#first-start), PyLoTs main window is shown. It defaults to a view of the [Waveform Plot](#waveform-plot), which starts empty.
+After entering the [information](#first-start), PyLoTs main window is shown. It defaults to a view of
+the [Waveform Plot](#waveform-plot), which starts empty.
 
 <img src=images/gui/pylot-main-screen.png alt="Tune autopicks button" title="Tune autopicks button">
 
@@ -61,24 +63,21 @@ Add trace data by [loading a project](#projects-and-events) or by [adding event
 ### Waveform Plot
 
 The waveform plot shows a trace list of all stations of an event.   
-Click on any trace to open the stations [picking window](#picking-window), where you can review automatic and manual picks.
+Click on any trace to open the stations [picking window](#picking-window), where you can review automatic and manual
+picks.
 
 <img src=images/gui/pylot-waveform-plot.png alt="A Waveform Plot showing traces of one event">
 
-Above the traces the currently displayed event can be selected.
+Above the traces the currently displayed event can be selected. In the bottom bar information about the trace under the
-In the bottom bar information about the trace under the mouse cursor is shown. This information includes the station name (station), the absolute UTC time (T) of the point under the mouse cursor and the relative time since the first trace start in seconds (t) as well as a trace count.
+mouse cursor is shown. This information includes the station name (station), the absolute UTC time (T) of the point
+under the mouse cursor and the relative time since the first trace start in seconds (t) as well as a trace count.
 
 #### Mouse view controls
 
 Hold left mouse button and drag to pan view.
 
-Hold right mouse button and
+Hold right mouse button and Direction | Result --- | --- Move the mouse up | Increase amplitude scale Move the mouse
-Direction | Result
+down | Decrease amplitude scale Move the mouse right | Increase time scale Move the mouse left | Decrease time scale
---- | ---
-Move the mouse up | Increase amplitude scale
-Move the mouse down | Decrease amplitude scale
-Move the mouse right | Increase time scale
-Move the mouse left | Decrease time scale
 
 Press right mouse button and click "View All" from the context menu to reset the view.
 
@@ -86,81 +85,100 @@ Press right mouse button and click "View All" from the context menu to reset the
 
 [//]: <> (Hack: We need these invisible spaces to add space to the first column, otherwise )
 
-Icon &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | Description
+| Icon &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | Description                                                                                                                                                                                                                                                                                                                                                                                                |
---- | ---
+|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-<img src="../icons/newfile.png" alt="Create new project" width="64" height="64"> | Create a new project, for more information about projects see [Projects and Events](#projects-and-events).
+| <img src="../icons/newfile.png" alt="Create new project" width="64" height="64">                                                                                                   | Create a new project, for more information about projects see [Projects and Events](#projects-and-events).                                                                                                                                                                                                                                                                                                 |
-<img src="../icons/openproject.png" alt="Open project" width="64" height="64"> | Load a project file from disk.
+| <img src="../icons/openproject.png" alt="Open project" width="64" height="64">                                                                                                     | Load a project file from disk.                                                                                                                                                                                                                                                                                                                                                                             |
-<img src="../icons/saveproject.png" alt="Save Project" width="64" height="64"> | Save all current events into an associated project file on disk. If there is no project file currently associated, you will be asked to create a new one.
+| <img src="../icons/saveproject.png" alt="Save Project" width="64" height="64">                                                                                                     | Save all current events into an associated project file on disk. If there is no project file currently associated, you will be asked to create a new one.                                                                                                                                                                                                                                                  |
-<img src="../icons/saveprojectas.png" alt="Save Project as" width="64" height="64"> | Save all current events into a new project file on disk. See [Saving projects](#saving-projects).
+| <img src="../icons/saveprojectas.png" alt="Save Project as" width="64" height="64">                                                                                                | Save all current events into a new project file on disk. See [Saving projects](#saving-projects).                                                                                                                                                                                                                                                                                                          |
-<img src="../icons/add.png" alt="Add event data" width="64" height="64"> | Add event data by selecting directories containing waveforms. For more information see [Event folder structure](#event-folder-structure).
+| <img src="../icons/add.png" alt="Add event data" width="64" height="64">                                                                                                           | Add event data by selecting directories containing waveforms. For more information see [Event folder structure](#event-folder-structure).                                                                                                                                                                                                                                                                  |
-<img src="../icons/openpick.png" alt="Load event information" width="64" height="64"> | Load picks/origins from disk into the currently displayed event. If a pick already exists for a station, the one from file will overwrite the existing one.
+| <img src="../icons/openpick.png" alt="Load event information" width="64" height="64">                                                                                              | Load picks/origins from disk into the currently displayed event. If a pick already exists for a station, the one from file will overwrite the existing one.                                                                                                                                                                                                                                                |
-<img src="../icons/openpicks.png" alt="Load information for all events" width="64" height="64"> | Load picks/origins for all events of the current project. PyLoT searches for files within the directory of the event and tries to load them for that event. For this function to work, the files containing picks/origins have to be named as described in [Event folder   structure](#event-folder-structure). If a pick already exists for a station, the one from file will overwrite the existing one.
+| <img src="../icons/openpicks.png" alt="Load information for all events" width="64" height="64">                                                                                    | Load picks/origins for all events of the current project. PyLoT searches for files within the directory of the event and tries to load them for that event. For this function to work, the files containing picks/origins have to be named as described in [Event folder   structure](#event-folder-structure). If a pick already exists for a station, the one from file will overwrite the existing one. |
-<img src="../icons/savepicks.png" alt="Save picks" width="64" height="64"> | Save event information such as picks and origin to file. You will be asked to select a directory in which this information should be saved.
+| <img src="../icons/savepicks.png" alt="Save picks" width="64" height="64">                                                                                                         | Save event information such as picks and origin to file. You will be asked to select a directory in which this information should be saved.                                                                                                                                                                                                                                                                |
-<img src="../icons/openloc.png" alt="Load location information" width="64" height="64"> | Load location information from disk,
+| <img src="../icons/openloc.png" alt="Load location information" width="64" height="64">                                                                                            | Load location information from disk,                                                                                                                                                                                                                                                                                                                                                                       |
-<img src="../icons/Matlab_PILOT_icon.png" alt="Load legacy information" width="64" height="64"> | Load event information from a previous, MatLab based PILOT version.
+| <img src="../icons/Matlab_PILOT_icon.png" alt="Load legacy information" width="64" height="64">                                                                                    | Load event information from a previous, MatLab based PILOT version.                                                                                                                                                                                                                                                                                                                                        |
-<img src="../icons/key_Z.png" alt="Display Z" width="64" height="64"> | Display Z component of streams in waveform plot.
+| <img src="../icons/key_Z.png" alt="Display Z" width="64" height="64">                                                                                                              | Display Z component of streams in waveform plot.                                                                                                                                                                                                                                                                                                                                                           |
-<img src="../icons/key_N.png" alt="Display N" width="64" height="64"> | Display N component of streams in waveform plot.
+| <img src="../icons/key_N.png" alt="Display N" width="64" height="64">                                                                                                              | Display N component of streams in waveform plot.                                                                                                                                                                                                                                                                                                                                                           |
-<img src="../icons/key_E.png" alt="Display E" width="64" height="64"> | Display E component of streams in waveform plot.
+| <img src="../icons/key_E.png" alt="Display E" width="64" height="64">                                                                                                              | Display E component of streams in waveform plot.                                                                                                                                                                                                                                                                                                                                                           |
-<img src="../icons/tune.png" alt="Tune Autopicker" width="64" height="64"> | Open the [Tune Autopicker window](#tuning).
+| <img src="../icons/tune.png" alt="Tune Autopicker" width="64" height="64">                                                                                                         | Open the [Tune Autopicker window](#tuning).                                                                                                                                                                                                                                                                                                                                                                |
-<img src="../icons/autopylot_button.png" alt="" width="64" height="64"> | Opens a window that allows starting the autopicker for all events ([Production run of the AutoPicker](#production-run-of-the-autopicker)).
+| <img src="../icons/autopylot_button.png" alt="" width="64" height="64">                                                                                                            | Opens a window that allows starting the autopicker for all events ([Production run of the AutoPicker](#production-run-of-the-autopicker)).                                                                                                                                                                                                                                                                 |
-<img src="../icons/compare_button.png" alt="Comparison" width="64" height="64"> | Compare automatic and manual picks, only available if automatic and manual picks for an event exist. See [Comparison between automatic and manual picks](#comparison-between-automatic-and-manual-picks).
+| <img src="../icons/compare_button.png" alt="Comparison" width="64" height="64">                                                                                                    | Compare automatic and manual picks, only available if automatic and manual picks for an event exist. See [Comparison between automatic and manual picks](#comparison-between-automatic-and-manual-picks).                                                                                                                                                                                                  |
-<img src="../icons/locate_button.png" alt="Locate event" width="64" height="64"> | Run a location routine (NonLinLoc) as configured in the settings on the picks. See [Location determination](#location-determination).
+| <img src="../icons/locate_button.png" alt="Locate event" width="64" height="64">                                                                                                   | Run a location routine (NonLinLoc) as configured in the settings on the picks. See [Location determination](#location-determination).                                                                                                                                                                                                                                                                      |
-
 
 ### Array Map
 
-The array map will display a color diagram to allow a visual check of the consistency of picks across multiple stations. This works by calculating the time difference of every onset to the earliest onset. Then isolines are drawn between stations with the same time difference and the areas between isolines are colored.  
+The array map will display a color diagram to allow a visual check of the consistency of picks across multiple stations.
-The result should resemble a color gradient as the wavefront rolls over the network area. Stations where picks are earlier/later than their neighbours can be reviewed by clicking on them, which opens the [picking window](#picking-window).
+This works by calculating the time difference of every onset to the earliest onset. Then isolines are drawn between
+stations with the same time difference and the areas between isolines are colored.  
+The result should resemble a color gradient as the wavefront rolls over the network area. Stations where picks are
+earlier/later than their neighbours can be reviewed by clicking on them, which opens
+the [picking window](#picking-window).
 
-Above the Array Map the picks that are used to create the map can be customized.
+Above the Array Map the picks that are used to create the map can be customized. The phase of picks that should be used
-The phase of picks that should be used can be selected, which allows checking the consistency of the P- and S-phase separately.
+can be selected, which allows checking the consistency of the P- and S-phase separately. Additionally the pick type can
-Additionally the pick type can be set to manual, automatic or hybrid, meaning display only manual picks, automatic picks or only display automatic picks for stations where there are no manual ones.
+be set to manual, automatic or hybrid, meaning display only manual picks, automatic picks or only display automatic
+picks for stations where there are no manual ones.
 
 ![Array Map](images/gui/arraymap-example.png "Array Map")  
-*Array Map for an event at the Northern Mid Atlantic Ridge, between North Africa and Mexico (Lat. 22.58, Lon. -45.11). The wavefront moved from west to east over the network area (Alps and Balcan region), with the earliest onsets in blue in the west.*
+*Array Map for an event at the Northern Mid Atlantic Ridge, between North Africa and Mexico (Lat. 22.58, Lon. -45.11).
+The wavefront moved from west to east over the network area (Alps and Balcan region), with the earliest onsets in blue
+in the west.*
 
-To be able to display an array map PyLoT needs to load an inventory file, where the metadata of seismic stations is kept. For more information see [Metadata](#adding-metadata). Additionally automatic or manual picks need to exist for the current event.
+To be able to display an array map PyLoT needs to load an inventory file, where the metadata of seismic stations is
+kept. For more information see [Metadata](#adding-metadata). Additionally automatic or manual picks need to exist for
+the current event.
 
 ### Eventlist
 
-The eventlist displays event parameters. The displayed parameters are saved in the .xml file in the event folder. Events can be deleted from the project by pressing the red X in the leftmost column of the corresponding event.
+The eventlist displays event parameters. The displayed parameters are saved in the .xml file in the event folder. Events
+can be deleted from the project by pressing the red X in the leftmost column of the corresponding event.
 
 <img src="images/gui/eventlist.png" alt="Eventlist">
 
-Column | Description
+| Column     | Description                                                                                                        |
---- | ---
+|------------|--------------------------------------------------------------------------------------------------------------------|
-Event | Full path to the events folder.
+| Event      | Full path to the events folder.                                                                                    |
-Time | Time of event.
+| Time       | Time of event.                                                                                                     |
-Lat | Latitude in degrees of event location.
+| Lat        | Latitude in degrees of event location.                                                                             |
-Lon | Longitude in degrees of event location.
+| Lon        | Longitude in degrees of event location.                                                                            |
-Depth | Depth in km of event.
+| Depth      | Depth in km of event.                                                                                              |
-Mag | Magnitude of event.
+| Mag        | Magnitude of event.                                                                                                |
-[N] MP | Number of manual picks.
+| [N] MP     | Number of manual picks.                                                                                            |
-[N] AP | Number of automatic picks.
+| [N] AP     | Number of automatic picks.                                                                                         |
-Tuning Set | Select whether this event is a Tuning event. See [Automatic Picking](#automatic-picking).
+| Tuning Set | Select whether this event is a Tuning event. See [Automatic Picking](#automatic-picking).                          |
-Test Set | Select whether this event is a Test event. See [Automatic Picking](#automatic-picking).
+| Test Set   | Select whether this event is a Test event. See [Automatic Picking](#automatic-picking).                            |
-Notes | Free form text field for notes regarding this event. Text will be saved in the notes.txt file in the event folder.
+| Notes      | Free form text field for notes regarding this event. Text will be saved in the notes.txt file in the event folder. |
 
 ## Usage
 
 ### Projects and Events
 
-PyLoT uses projects to categorize different seismic data. A project consists of one or multiple events. Events contain seismic traces from one or multiple stations. An event also contains further information, e.g. origin time, source parameters and automatic as well as manual picks.
+PyLoT uses projects to categorize different seismic data. A project consists of one or multiple events. Events contain
-Projects are used to group events which should be analysed together. A project could contain all events from a specific region within a timeframe of interest or all recorded events of a seismological experiment.
+seismic traces from one or multiple stations. An event also contains further information, e.g. origin time, source
+parameters and automatic as well as manual picks. Projects are used to group events which should be analysed together. A
+project could contain all events from a specific region within a timeframe of interest or all recorded events of a
+seismological experiment.
 
 ### Event folder structure
 
 PyLoT expects the following folder structure for seismic data:
+
 * Every event should be in it's own folder with the following naming scheme for the folders:
-   ``e[id].[doy].[yy]``, where ``[id]`` is a four-digit numerical id increasing from 0001, ``[doy]`` the three digit day of year and ``[yy]`` the last two digits of the year of the event. This structure has to be created by the user of PyLoT manually.
+  ``e[id].[doy].[yy]``, where ``[id]`` is a four-digit numerical id increasing from 0001, ``[doy]`` the three digit day
+  of year and ``[yy]`` the last two digits of the year of the event. This structure has to be created by the user of
+  PyLoT manually.
 * These folders should contain the seismic data for their event as ``.mseed`` or other supported filetype
-* All automatic and manual picks should be in an ``.xml`` file in their event folder. PyLoT saves picks in this file. This file does not have to be added manually unless there are picks to be imported. The format used to save picks is QUAKEML.   
+* All automatic and manual picks should be in an ``.xml`` file in their event folder. PyLoT saves picks in this file.
-Picks are saved in a file with the same filename as the event folder with ``PyLoT_`` prepended.
+  This file does not have to be added manually unless there are picks to be imported. The format used to save picks is
-* The file ``notes.txt`` is used for saving analysts comments. Everything saved here will be displayed in the 'Notes' column of the eventlist.
+  QUAKEML.   
+  Picks are saved in a file with the same filename as the event folder with ``PyLoT_`` prepended.
+* The file ``notes.txt`` is used for saving analysts comments. Everything saved here will be displayed in the 'Notes'
+  column of the eventlist.
 
 ### Loading event information from CSV file
 
-Event information can be saved in a ``.csv`` file located in the rootpath. The file is made from one header line, which is followed by one or multiple data lines. Values are separated by comma, while a dot is used as a decimal separator.   
+Event information can be saved in a ``.csv`` file located in the rootpath. The file is made from one header line, which
+is followed by one or multiple data lines. Values are separated by comma, while a dot is used as a decimal separator.   
 This information is then shown in the table in the [Eventlist tab](#Eventlist).
 
 One example header and data line is shown below.
@@ -169,50 +187,63 @@ One example header and data line is shown below.
 
 The meaning of the header entries is:
 
-Header | description
+| Header               | description                                                                                    |
---- | ---
+|----------------------|------------------------------------------------------------------------------------------------|
-event | Event id, has to be the same as the folder name in which waveform data for this event is kept.
+| event                | Event id, has to be the same as the folder name in which waveform data for this event is kept. |
-Data | Origin date of the event, format DD/MM/YY or DD/MM/YYYY.
+| Data                 | Origin date of the event, format DD/MM/YY or DD/MM/YYYY.                                       |
-Time | Origin time of the event. Format HH:MM:SS.
+| Time                 | Origin time of the event. Format HH:MM:SS.                                                     |
-Lat, Long | Origin latitude and longitude in decimal degrees.
+| Lat, Long            | Origin latitude and longitude in decimal degrees.                                              |
-Region | Flinn-Engdahl region name.
+| Region               | Flinn-Engdahl region name.                                                                     |
-Basis Lat, Basis Lon | Latitude and longitude of the basis of the station network in decimal degrees.
+| Basis Lat, Basis Lon | Latitude and longitude of the basis of the station network in decimal degrees.                 |
-Distance [km] | Distance from origin coordinates to basis coordinates in km.
+| Distance [km]        | Distance from origin coordinates to basis coordinates in km.                                   |
-Distance [rad] | Distance from origin coordinates to basis coordinates in rad.
+| Distance [rad]       | Distance from origin coordinates to basis coordinates in rad.                                  |
-
 
 ### Adding events to project
 
-PyLoT GUI starts with an empty project. To add events, use the add event data button. Select one or multiple folders containing events. 
+PyLoT GUI starts with an empty project. To add events, use the add event data button. Select one or multiple folders
+containing events.
 
 [//]: <> (TODO: explain _Directories: Root path, Data path, Database path_)
 
 ### Saving projects
 
-Save the current project from the menu with File->Save project or File->Save project as.
+Save the current project from the menu with File->Save project or File->Save project as. PyLoT uses ``.plp`` files to
-PyLoT uses ``.plp`` files to save project information. This file format is not interchangeable between different versions of Python interpreters.
+save project information. This file format is not interchangeable between different versions of Python interpreters.
-Saved projects contain the automatic and manual picks. Seismic trace data is not included into the ``.plp`` file, but read from its location used when saving the file.
+Saved projects contain the automatic and manual picks. Seismic trace data is not included into the ``.plp`` file, but
+read from its location used when saving the file.
 
 ### Adding metadata
 
 [//]: <> (TODO: Add picture of metadata "manager" when it is done)
 
-PyLoT can handle ``.dless``, ``.xml``, ``.resp`` and ``.dseed`` file formats for Metadata. Metadata files stored on disk can be added to a project by clicking *Edit*->*Manage Inventories*. This opens up a window where the folders which contain metadata files can be selected. PyLoT will then search these files for the station names when it needs the information.
+PyLoT can handle ``.dless``, ``.xml``, ``.resp`` and ``.dseed`` file formats for Metadata. Metadata files stored on disk
+can be added to a project by clicking *Edit*->*Manage Inventories*. This opens up a window where the folders which
+contain metadata files can be selected. PyLoT will then search these files for the station names when it needs the
+information.
 
 # Picking
 
 PyLoTs automatic and manual pick determination works as following:
-* Using certain parameters, a first initial/coarse pick is determined. The first manual pick is determined by visual review of the whole waveform and selection of the most likely onset by the analyst. The first automatic pick is determined by calculation of a characteristic function (CF) for the seismic trace. When a wave arrives, the CFs properties change, which is determined as the signals onset.
+
-* Afterwards, a refined set of parameters is applied to a small part of the waveform around the initial onset. For manual picks this means a closer view of the trace, for automatic picks this is done by a recalculated CF with different parameters.
+* Using certain parameters, a first initial/coarse pick is determined. The first manual pick is determined by visual
+  review of the whole waveform and selection of the most likely onset by the analyst. The first automatic pick is
+  determined by calculation of a characteristic function (CF) for the seismic trace. When a wave arrives, the CFs
+  properties change, which is determined as the signals onset.
+* Afterwards, a refined set of parameters is applied to a small part of the waveform around the initial onset. For
+  manual picks this means a closer view of the trace, for automatic picks this is done by a recalculated CF with
+  different parameters.
 * This second picking phase results in the precise pick, which is treated as the onset time.
 
 ## Manual Picking
 
-To create manual picks, you will need to open or create a project that contains seismic trace data (see [Adding events to projects](#adding-events-to-project)). Click on a trace to open the [Picking window](#picking-window).
+To create manual picks, you will need to open or create a project that contains seismic trace data (
+see [Adding events to projects](#adding-events-to-project)). Click on a trace to open
+the [Picking window](#picking-window).
 
 ### Picking window
 
-Open the picking window of a station by leftclicking on any trace in the waveform plot. Here you can create manual picks for the selected station. 
+Open the picking window of a station by leftclicking on any trace in the waveform plot. Here you can create manual picks
+for the selected station.
 
 <img src="images/gui/picking/pickwindow.png" alt="Picking window">
 
@@ -220,24 +251,24 @@ Open the picking window of a station by leftclicking on any trace in the wavefor
 
 #### Picking Window Settings
 
-Icon | Shortcut | Menu Alternative | Description
+| Icon                                                                             | Shortcut       | Menu Alternative            | Description                                                                                                                                                                                                         |
----|---|---|---
+|----------------------------------------------------------------------------------|----------------|-----------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-<img src="../icons/filter_p.png" alt="Filter P" width="64" height="64"> | p | Filter->Apply P Filter | Filter all channels according to the options specified in Filter parameter, P Filter section.
+| <img src="../icons/filter_p.png" alt="Filter P" width="64" height="64">          | p              | Filter->Apply P Filter      | Filter all channels according to the options specified in Filter parameter, P Filter section.                                                                                                                       |
-<img src="../icons/filter_s.png" alt="Filter S" width="64" height="64"> | s | Filter->Apply S Filter | Filter all channels according to the options specified in Filter parameter, S Filter section.
+| <img src="../icons/filter_s.png" alt="Filter S" width="64" height="64">          | s              | Filter->Apply S Filter      | Filter all channels according to the options specified in Filter parameter, S Filter section.                                                                                                                       |
-<img src="../icons/key_A.png" alt="Filter Automatically" width="64" height="64"> | Ctrl + a | Filter->Automatic Filtering | If enabled, automatically select the correct filter option (P, S) depending on the selected phase to be picked.
+| <img src="../icons/key_A.png" alt="Filter Automatically" width="64" height="64"> | Ctrl + a       | Filter->Automatic Filtering | If enabled, automatically select the correct filter option (P, S) depending on the selected phase to be picked.                                                                                                     |
-![desc](images/gui/picking/phase_selection.png "Phase selection") | 1 (P) or 5 (S) | Picks->P or S | Select phase to pick. If Automatic Filtering is enabled, this will apply the appropriate filter depending on the phase.
+| ![desc](images/gui/picking/phase_selection.png "Phase selection")                | 1 (P) or 5 (S) | Picks->P or S               | Select phase to pick. If Automatic Filtering is enabled, this will apply the appropriate filter depending on the phase.                                                                                             |
-![Zoom into](../icons/zoom_in.png "Zoom into waveform") | - | - | Zoom into waveform.
+| ![Zoom into](../icons/zoom_in.png "Zoom into waveform")                          | -              | -                           | Zoom into waveform.                                                                                                                                                                                                 |
-![Reset zoom](../icons/zoom_0.png "Reset zoom") | - | - | Reset zoom to default view.
+| ![Reset zoom](../icons/zoom_0.png "Reset zoom")                                  | -              | -                           | Reset zoom to default view.                                                                                                                                                                                         |
-![Delete picks](../icons/delete.png "Delete picks") | - | - | Delete all manual picks on this station.
+| ![Delete picks](../icons/delete.png "Delete picks")                              | -              | -                           | Delete all manual picks on this station.                                                                                                                                                                            |
-![Rename a phase](../icons/sync.png "Rename a phase") | - | - | Click this button and then the picked phase to rename it.
+| ![Rename a phase](../icons/sync.png "Rename a phase")                            | -              | -                           | Click this button and then the picked phase to rename it.                                                                                                                                                           |
-![Continue](images/gui/picking/continue.png "Continue with next station") | - | - | If checked, after accepting the manual picks for this station with 'OK', the picking window for the next station will be opened. This option is useful for fast manual picking of a complete event.
+| ![Continue](images/gui/picking/continue.png "Continue with next station")        | -              | -                           | If checked, after accepting the manual picks for this station with 'OK', the picking window for the next station will be opened. This option is useful for fast manual picking of a complete event.                 |
-Estimated onsets | - | - | Show the theoretical onsets for this station. Needs metadata and origin information.
+| Estimated onsets                                                                 | -              | -                           | Show the theoretical onsets for this station. Needs metadata and origin information.                                                                                                                                |
-Compare to channel | - | - | Select a data channel to compare against. The selected channel will be displayed in the picking window behind every channel allowing the analyst to visually compare signal correlation between different channels.
+| Compare to channel                                                               | -              | -                           | Select a data channel to compare against. The selected channel will be displayed in the picking window behind every channel allowing the analyst to visually compare signal correlation between different channels. |
-Scaling | - | - | Individual means every channel is scaled to its own maximum. If a channel is selected here, all channels will be scaled relatively to this channel.
+| Scaling                                                                          | -              | -                           | Individual means every channel is scaled to its own maximum. If a channel is selected here, all channels will be scaled relatively to this channel.                                                                 |
 
-Menu Command | Shortcut | Description
+| Menu Command              | Shortcut | Description                                                                                                                                                                                                                                                                 |
----|---|---
+|---------------------------|----------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-P Channels and S Channels | - | Select which channels should be treated as P or S channels during picking. When picking a phase, only the corresponding channels will be shown during the precise pick. Normally, the Z channel should be selected for the P phase and the N and E channel for the S phase. 
+| P Channels and S Channels | -        | Select which channels should be treated as P or S channels during picking. When picking a phase, only the corresponding channels will be shown during the precise pick. Normally, the Z channel should be selected for the P phase and the N and E channel for the S phase. |
 
 ### Filtering
 
@@ -245,105 +276,167 @@ Access the Filter options by pressing Ctrl+f on the Waveform plot or by the menu
 
 <img src=images/gui/pylot-filter-options.png>
 
- Here you are able to select filter type, order and frequencies for the P and S pick separately. These settings are used in the GUI for displaying the filtered waveform data and during manual picking. The values used by PyLoT for automatic picking are displayed next to the manual values. They can be changed in the [Tune Autopicker dialog](#tuning).  
+Here you are able to select filter type, order and frequencies for the P and S pick separately. These settings are used
-A green value automatic value means the automatic and manual filter parameter is configured the same, red means they are configured differently.
+in the GUI for displaying the filtered waveform data and during manual picking. The values used by PyLoT for automatic
-By toggling the "Overwrite filteroptions" checkmark you can set whether the manual precise/second pick uses the filter settings for the automatic picker (unchecked) or whether it uses the filter options in this dialog (checked).
+picking are displayed next to the manual values. They can be changed in the [Tune Autopicker dialog](#tuning).  
-To guarantee consistent picking results between automatic and manual picking it is recommended to use the same filter settings for the determination of automatic and manual picks.
+A green value automatic value means the automatic and manual filter parameter is configured the same, red means they are
+configured differently. By toggling the "Overwrite filteroptions" checkmark you can set whether the manual
+precise/second pick uses the filter settings for the automatic picker (unchecked) or whether it uses the filter options
+in this dialog (checked). To guarantee consistent picking results between automatic and manual picking it is recommended
+to use the same filter settings for the determination of automatic and manual picks.
 
 ### Export and Import of manual picks
 
 #### Export
 
-After the creation of manual picks they can either be saved in the project file (see [Saving projects](#saving-projects)). Alternatively the picks can be exported by pressing the <img src="../icons/savepicks.png" alt="Save event information button" title="Save picks button" height=24 width=24> button above the waveform plot or in the menu File->Save event information (shortcut Ctrl+p). Select the event directory in which to save the file. The filename will be ``PyLoT_[event_folder_name].[filetype selected during first startup]``.     
+After the creation of manual picks they can either be saved in the project file (
-You can rename and copy this file, but PyLoT will then no longer be able to automatically recognize the correct picks for an event and the file will have to be manually selected when loading. 
+see [Saving projects](#saving-projects)). Alternatively the picks can be exported by pressing
+the <img src="../icons/savepicks.png" alt="Save event information button" title="Save picks button" height=24 width=24>
+button above the waveform plot or in the menu File->Save event information (shortcut Ctrl+p). Select the event directory
+in which to save the file. The filename will be ``PyLoT_[event_folder_name].[filetype selected during first startup]``
+.     
+You can rename and copy this file, but PyLoT will then no longer be able to automatically recognize the correct picks
+for an event and the file will have to be manually selected when loading.
 
 #### Import
 
-To import previously saved picks press the <img src="../icons/openpick.png" alt="Load event information button" width="24" height="24"> button and select the file to load. You will be asked to save the current state of your project if you have not done so before. You can continue without saving by pressing "Discard". This does not delete any information from your project, it just means that no project file is saved before the changes of importing picks are applied.
+To import previously saved picks press
-PyLoT will automatically load files named after the scheme it uses when saving picks, described in the paragraph above. If it can't find any matching files, a file dialogue will open and you can select the file you wish to load. 
+the <img src="../icons/openpick.png" alt="Load event information button" width="24" height="24"> button and select the
+file to load. You will be asked to save the current state of your project if you have not done so before. You can
+continue without saving by pressing "Discard". This does not delete any information from your project, it just means
+that no project file is saved before the changes of importing picks are applied. PyLoT will automatically load files
+named after the scheme it uses when saving picks, described in the paragraph above. If it can't find any matching files,
+a file dialogue will open and you can select the file you wish to load.
 
-If you see a warning "Mismatch in event identifiers" and are asked whether to continue loading the picks, this means that PyLoT doesn't recognize the picks in the file as belonging to this specific event. They could have either been saved under a different installation of PyLoT but with the same waveform data, which means they are still compatible and you can continue loading them. Or they could be picks from a different event, in which case loading them is not recommended. 
+If you see a warning "Mismatch in event identifiers" and are asked whether to continue loading the picks, this means
+that PyLoT doesn't recognize the picks in the file as belonging to this specific event. They could have either been
+saved under a different installation of PyLoT but with the same waveform data, which means they are still compatible and
+you can continue loading them. Or they could be picks from a different event, in which case loading them is not
+recommended.
 
 ## Automatic Picking
 
-
 The general workflow for automatic picking is as following:
-- After setting up the project by loading waveforms and optionally metadata, the right parameters for the autopicker have to be determined
+
+- After setting up the project by loading waveforms and optionally metadata, the right parameters for the autopicker
+  have to be determined
 - This [tuning](#tuning) is done for single stations with immediate graphical feedback of all picking results
 - Afterwards the autopicker can be run for all or a subset of events from the project
 
-For automatic picking PyLoT discerns between tune and test events, which the user has to set as such. Tune events are used to calibrate the autopicking algorithm, test events are then used to test the calibration. The purpose of that is controlling whether the parameters found during tuning are able to reliably pick the "unknown" test events.  
+For automatic picking PyLoT discerns between tune and test events, which the user has to set as such. Tune events are
-If this behaviour is not desired and all events should be handled the same, dont mark any events. Since this is just a way to group events to compare the picking results, nothing else will change.
+used to calibrate the autopicking algorithm, test events are then used to test the calibration. The purpose of that is
+controlling whether the parameters found during tuning are able to reliably pick the "unknown" test events.  
+If this behaviour is not desired and all events should be handled the same, dont mark any events. Since this is just a
+way to group events to compare the picking results, nothing else will change.
 
 ### Tuning
 
-Tuning describes the process of adjusting the autopicker settings to the characteristics of your data set. To do this in PyLoT, use the <img src=../icons/tune.png height=24 alt="Tune autopicks button" title="Tune autopicks button"> button to open the Tune Autopicker. 
+Tuning describes the process of adjusting the autopicker settings to the characteristics of your data set. To do this in
+PyLoT, use the <img src=../icons/tune.png height=24 alt="Tune autopicks button" title="Tune autopicks button"> button to
+open the Tune Autopicker.
 
 <img src=images/gui/tuning/tune_autopicker.png>
 
 View of a station in the Tune Autopicker window.
+
 1. Select the event to be displayed and processed.
 2. Select the station from the event.
-3. To pick the currently displayed trace, click the <img src=images/gui/tuning/autopick_trace_button.png alt="Pick trace button" title="Autopick trace button" height=16> button.
+3. To pick the currently displayed trace, click
-4. These tabs are used to select the current view. __Traces Plot__ contains a plot of the stations traces, where manual picks can be created/edited. __Overview__ contains graphical results of the automatic picking process. The __P and S tabs__ contain the automatic picking results of the P and S phase, while __log__ contains a useful text output of automatic picking.
+   the <img src=images/gui/tuning/autopick_trace_button.png alt="Pick trace button" title="Autopick trace button" height=16>
-5. These buttons are used to load/save/reset settings for automatic picking. The parameters can be saved in PyLoT input files, which have the file ending *.in*. They are human readable text files, which can also be edited by hand. Saving the parameters allows you to load them again later, even on different machines.
+   button.
-6. These menus control the behaviour of the creation of manual picks from the Tune Autopicker window. Picks allows to select the phase for which a manual pick should be created, Filter allows to filter waveforms and edit the filter parameters. P-Channels and S-Channels allow to select the channels that should be displayed when creating a manual P or S pick. 
+4. These tabs are used to select the current view. __Traces Plot__ contains a plot of the stations traces, where manual
-7. This menu is the same as in the [Picking Window](#picking-window-settings), with the exception of the __Manual Onsets__ options. The __Manual Onsets__ buttons accepts or reject the manual picks created in the Tune Autopicker window, pressing accept adds them to the manual picks for the event, while reject removes them.   
+   picks can be created/edited. __Overview__ contains graphical results of the automatic picking process. The __P and S
+   tabs__ contain the automatic picking results of the P and S phase, while __log__ contains a useful text output of
+   automatic picking.
+5. These buttons are used to load/save/reset settings for automatic picking. The parameters can be saved in PyLoT input
+   files, which have the file ending *.in*. They are human readable text files, which can also be edited by hand. Saving
+   the parameters allows you to load them again later, even on different machines.
+6. These menus control the behaviour of the creation of manual picks from the Tune Autopicker window. Picks allows to
+   select the phase for which a manual pick should be created, Filter allows to filter waveforms and edit the filter
+   parameters. P-Channels and S-Channels allow to select the channels that should be displayed when creating a manual P
+   or S pick.
+7. This menu is the same as in the [Picking Window](#picking-window-settings), with the exception of the __Manual
+   Onsets__ options. The __Manual Onsets__ buttons accepts or reject the manual picks created in the Tune Autopicker
+   window, pressing accept adds them to the manual picks for the event, while reject removes them.
 8. The traces plot in the centre allows creating manual picks and viewing the waveforms.
-9. The parameters which influence the autopicking result are in the Main settings and Advanced settings tabs on the left side. For a description of all the parameters see the [tuning documentation](tuning.md).
+9. The parameters which influence the autopicking result are in the Main settings and Advanced settings tabs on the left
+   side. For a description of all the parameters see the [tuning documentation](tuning.md).
 
 ### Production run of the autopicker
 
-After the settings used during tuning give the desired results, the autopicker can be used on the complete dataset. To invoke the autopicker on the whole set of events, click the <img src=../icons/autopylot_button.png alt="Autopick" title="Autopick" height=32> button.
+After the settings used during tuning give the desired results, the autopicker can be used on the complete dataset. To
+invoke the autopicker on the whole set of events, click
+the <img src=../icons/autopylot_button.png alt="Autopick" title="Autopick" height=32> button.
 
 ### Evaluation of automatic picks
 
 PyLoT has two internal consistency checks for automatic picks that were determined for an event:
+
 1. Jackknife check
 2. Wadati check
 
 #### 1. Jackknife check
 
-The jackknife test in PyLoT checks the consistency of automatically determined P-picks by checking the statistical variance of the picks. The variance of all P-picks is calculated and compared to the variance of subsets, in which one pick is removed.   
+The jackknife test in PyLoT checks the consistency of automatically determined P-picks by checking the statistical
-The idea is, that picks that are close together in time should not influence the estimation of the variance much, while picks whose positions deviates from the norm influence the variance to a greater extent. If the estimated variance of a subset with a pick removed differs to much from the estimated variance of all picks, the pick that was removed from the subset will be marked as invalid.   
+variance of the picks. The variance of all P-picks is calculated and compared to the variance of subsets, in which one
-The factor by which picks are allowed to skew from the estimation of variance can be configured, it is called *jackfactor*, see [here](tuning.md#Pick-quality-control).
+pick is removed.   
+The idea is, that picks that are close together in time should not influence the estimation of the variance much, while
+picks whose positions deviates from the norm influence the variance to a greater extent. If the estimated variance of a
+subset with a pick removed differs to much from the estimated variance of all picks, the pick that was removed from the
+subset will be marked as invalid.   
+The factor by which picks are allowed to skew from the estimation of variance can be configured, it is called *
+jackfactor*, see [here](tuning.md#Pick-quality-control).
 
-Additionally, the deviation of picks from the median is checked. For that, the median of all P-picks that passed the Jackknife test is calculated. Picks whose onset times deviate from the mean onset time by more than the *mdttolerance* are marked as invalid.
+Additionally, the deviation of picks from the median is checked. For that, the median of all P-picks that passed the
+Jackknife test is calculated. Picks whose onset times deviate from the mean onset time by more than the *mdttolerance*
+are marked as invalid.
 
 <img src=images/gui/jackknife_plot.png title="Jackknife/Median test diagram">
 
-*The result of both tests (Jackknife and Median) is shown in a diagram afterwards. The onset time is plotted against a running number of stations. Picks that failed either the Jackknife or the median test are colored red. The median is plotted as a green line.* 
+*The result of both tests (Jackknife and Median) is shown in a diagram afterwards. The onset time is plotted against a
+running number of stations. Picks that failed either the Jackknife or the median test are colored red. The median is
+plotted as a green line.*
 
-The Jackknife and median check are suitable to check for picks that are outside of the expected time window, for example, when a wrong phase was picked. It won't recognize picks that are in close proximity to the right onset which are just slightly to late/early.
+The Jackknife and median check are suitable to check for picks that are outside of the expected time window, for
+example, when a wrong phase was picked. It won't recognize picks that are in close proximity to the right onset which
+are just slightly to late/early.
 
 #### 2. Wadati check
 
-The Wadati check checks the consistency of S picks. For this the SP-time, the time difference between S and P onset is plotted against the P onset time. A line is fitted to the points, which minimizes the error. Then the deviation of single picks to this line is checked. If the deviation in seconds is above the *wdttolerance* parameter ([see here](tuning.md#Pick-quality-control)), the pick is marked as invalid.
+The Wadati check checks the consistency of S picks. For this the SP-time, the time difference between S and P onset is
+plotted against the P onset time. A line is fitted to the points, which minimizes the error. Then the deviation of
+single picks to this line is checked. If the deviation in seconds is above the *wdttolerance*
+parameter ([see here](tuning.md#Pick-quality-control)), the pick is marked as invalid.
 
 <img src=images/gui/wadati_plot.png title="Output diagram of Wadati check">
 
-*The Wadati plot in PyLoT shows the SP onset time difference over the P onset time. A first line is fitted (black). All picks which deviate to much from this line are marked invalid (red). Then a second line is fitted which excludes the invalid picks. From this lines slope, the ratio of P and S wave velocity is determined.*
+*The Wadati plot in PyLoT shows the SP onset time difference over the P onset time. A first line is fitted (black). All
+picks which deviate to much from this line are marked invalid (red). Then a second line is fitted which excludes the
+invalid picks. From this lines slope, the ratio of P and S wave velocity is determined.*
 
 ### Comparison between automatic and manual picks
 
-Every pick in PyLoT consists of an earliest possible, latest possible and most likely onset time. 
+Every pick in PyLoT consists of an earliest possible, latest possible and most likely onset time. The earliest and
-The earliest and latest possible onset time characterize the uncertainty of a pick. 
+latest possible onset time characterize the uncertainty of a pick. This approach is described in Diel, Kissling and
-This approach is described in Diel, Kissling and Bormann (2012) - Tutorial for consistent phase picking at local to regional distances. 
+Bormann (2012) - Tutorial for consistent phase picking at local to regional distances. These times are represented as a
-These times are represented as a Probability Density Function (PDF) for every pick. 
+Probability Density Function (PDF) for every pick. The PDF is implemented as two exponential distributions around the
-The PDF is implemented as two exponential distributions around the most likely onset as the expected value. 
+most likely onset as the expected value.
 
-To compare two single picks, their PDFs are cross correlated to create a new PDF. 
+To compare two single picks, their PDFs are cross correlated to create a new PDF. This corresponds to the subtraction of
-This corresponds to the subtraction of the automatic pick from the manual pick.
+the automatic pick from the manual pick.
 
  <img src=images/gui/comparison/comparison_pdf.png title="Comparison between automatic and manual pick">
 
- *Comparison between an automatic and a manual pick for a station in PyLoT by comparing their PDFs.*  
+*Comparison between an automatic and a manual pick for a station in PyLoT by comparing their PDFs.*  
- *The upper plot shows the difference between the two single picks that are shown in the lower plot.*
+*The upper plot shows the difference between the two single picks that are shown in the lower plot.*
- *The difference is implemented as a cross correlation between the two PDFs. and results in a new PDF, the comparison PDF.*
+*The difference is implemented as a cross correlation between the two PDFs. and results in a new PDF, the comparison
- *The expected value of the comparison PDF corresponds to the time distance between the automatic and manual picks most likely onset.*
+PDF.*
- *The standard deviation corresponds to the combined uncertainty.*
+*The expected value of the comparison PDF corresponds to the time distance between the automatic and manual picks most
+likely onset.*
+*The standard deviation corresponds to the combined uncertainty.*
 
-To compare the automatic and manual picks between multiple stations of an event, the properties of all the comparison PDFs are shown in a histogram.
+To compare the automatic and manual picks between multiple stations of an event, the properties of all the comparison
+PDFs are shown in a histogram.
 
 <img src=images/gui/comparison/compare_widget.png title="Comparison between picks of an event">
 
@@ -352,11 +445,13 @@ To compare the automatic and manual picks between multiple stations of an event,
 *The bottom left plot shows the expected values of the comparison PDFs for P picks.*
 *The top right plot shows the standard deviation of the comparison PDFs for S picks.*
 *The bottom right plot shows the expected values of the comparison PDFs for S picks.*   
-*The standard deviation plots show that most P picks have an uncertainty between 1 and 2 seconds, while S pick uncertainties have a much larger spread between 1 to 15 seconds.* 
+*The standard deviation plots show that most P picks have an uncertainty between 1 and 2 seconds, while S pick
+uncertainties have a much larger spread between 1 to 15 seconds.*
 *This means P picks have higher quality classes on average than S picks.*
-*The expected values are largely negative, meaning that the algorithm tends to pick earlier than the analyst with the applied settings (Manual - Automatic).*
+*The expected values are largely negative, meaning that the algorithm tends to pick earlier than the analyst with the
-*The number of samples mentioned in the plots legends is the amount of stations that have an automatic and a manual P pick.*
+applied settings (Manual - Automatic).*
-
+*The number of samples mentioned in the plots legends is the amount of stations that have an automatic and a manual P
+pick.*
 
 ### Export and Import of automatic picks
 
@@ -369,7 +464,11 @@ To be added.
 # FAQ
 
 Q: During manual picking the error "No channel to plot for phase ..." is displayed, and I am unable to create a pick.  
-A: Select a channel that should be used for the corresponding phase in the Pickwindow. For further information read [Picking Window settings](#picking-window-settings).
+A: Select a channel that should be used for the corresponding phase in the Pickwindow. For further information
+read [Picking Window settings](#picking-window-settings).
 
 Q: I see a warning "Mismatch in event identifiers" when loading picks from a file.  
-A: This means that PyLoT doesn't recognize the picks in the file as belonging to this specific event. They could have been saved under a different installation of PyLoT but with the same waveform data, which means they are still compatible and you can continue loading them or they could be the picks of a different event, in which case loading them is not recommended.
+A: This means that PyLoT doesn't recognize the picks in the file as belonging to this specific event. They could have
+been saved under a different installation of PyLoT but with the same waveform data, which means they are still
+compatible and you can continue loading them or they could be the picks of a different event, in which case loading them
+is not recommended.

docs/tuning.md

@@ -6,95 +6,145 @@ A description of the parameters used for determining automatic picks.
 
 Parameters applied to the traces before picking algorithm starts.
 
-Name | Description
+| Name                | Description                                                                                                                                                                                                             |
---- | ---
+|---------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-*P Start*, *P Stop* | Define time interval relative to trace start time for CF calculation on vertical trace. Value is relative to theoretical onset time if 'Use TauPy' option is enabled in main settings of 'Tune Autopicker' dialogue.
+| *P Start*, *P
-*S Start*, *S Stop* | Define time interval relative to trace start time for CF calculation on horizontal traces. Value is relative to theoretical onset time if 'Use TauPy' option is enabled in main settings of 'Tune Autopicker' dialogue.
+Stop* | Define time interval relative to trace start time for CF calculation on vertical trace. Value is relative to theoretical onset time if 'Use TauPy' option is enabled in main settings of 'Tune Autopicker' dialogue.    |
-*Bandpass Z1* | Filter settings for Butterworth bandpass applied to vertical trace for calculation of initial P pick.
+| *S Start*, *S
-*Bandpass Z2* | Filter settings for Butterworth bandpass applied to vertical trace for calculation of precise P pick.
+Stop* | Define time interval relative to trace start time for CF calculation on horizontal traces. Value is relative to theoretical onset time if 'Use TauPy' option is enabled in main settings of 'Tune Autopicker' dialogue. |
-*Bandpass H1* | Filter settings for Butterworth bandpass applied to horizontal traces for calculation of initial S pick.
+| *Bandpass
-*Bandpass H2* | Filter settings for Butterworth bandpass applied to horizontal traces for calculation of precise S pick.
+Z1*       | Filter settings for Butterworth bandpass applied to vertical trace for calculation of initial P pick.                                                                                                                   |
+| *Bandpass
+Z2*       | Filter settings for Butterworth bandpass applied to vertical trace for calculation of precise P pick.                                                                                                                   |
+| *Bandpass
+H1*       | Filter settings for Butterworth bandpass applied to horizontal traces for calculation of initial S pick.                                                                                                                |
+| *Bandpass
+H2*       | Filter settings for Butterworth bandpass applied to horizontal traces for calculation of precise S pick.                                                                                                                |
 
 ## Inital P pick
 
 Parameters used for determination of initial P pick.
 
-Name | Description
+| Name         | Description                                                                                                                  |
---- | ---
+|--------------|------------------------------------------------------------------------------------------------------------------------------|
-*tLTA* | Size of gliding LTA window in seconds used for calculation of HOS-CF.
+| *
-*pickwin P* | Size of time window in seconds in which the minimum of the AIC-CF in front of the maximum of the HOS-CF is determined.
+tLTA*       | Size of gliding LTA window in seconds used for calculation of HOS-CF.                                                        |
-*AICtsmooth* | Average of samples in this time window will be used for smoothing of the AIC-CF.
+| *pickwin
-*checkwinP* | Time in front of the global maximum of the HOS-CF in which to search for a second local extrema.
+P*  | Size of time window in seconds in which the minimum of the AIC-CF in front of the maximum of the HOS-CF is determined.       |
-*minfactorP* | Used with *checkwinP*. If a second local maximum is found, it has to be at least as big as the first maximum * *minfactorP*.
+| *
-*tsignal* | Time window in seconds after the initial P pick used for determining signal amplitude.
+AICtsmooth* | Average of samples in this time window will be used for smoothing of the AIC-CF.                                             |
-*tnoise* | Time window in seconds in front of initial P pick used for determining noise amplitude.
+| *
-*tsafetey* | Time in seconds between *tsignal* and *tnoise*.
+checkwinP*  | Time in front of the global maximum of the HOS-CF in which to search for a second local extrema.                             |
-*tslope* | Time window in seconds after initial P pick in which the slope of the onset is calculated.
+| *minfactorP* | Used with *
+checkwinP*. If a second local maximum is found, it has to be at least as big as the first maximum * *minfactorP*. |
+| *
+tsignal*    | Time window in seconds after the initial P pick used for determining signal amplitude.                                       |
+| *
+tnoise*     | Time window in seconds in front of initial P pick used for determining noise amplitude.                                      |
+| *tsafetey*   | Time in seconds between *tsignal* and *
+tnoise*.                                                                              |
+| *
+tslope*     | Time window in seconds after initial P pick in which the slope of the onset is calculated.                                   |
 
 ## Inital S pick
 
 Parameters used for determination of initial S pick
 
-Name | Description
+| Name          | Description                                                                                                                  |
---- | ---
+|---------------|------------------------------------------------------------------------------------------------------------------------------|
-*tdet1h* | Length of time window in seconds in which AR params of the waveform are determined.
+| *
-*tpred1h* | Length of time window in seconds in which the waveform is predicted using the AR model.
+tdet1h*      | Length of time window in seconds in which AR params of the waveform are determined.                                          |
-*AICtsmoothS* | Average of samples in this time window is used for smoothing the AIC-CF.
+| *
-*pickwinS* | Time window in which the minimum in the AIC-CF in front of the maximum in the ARH-CF is determined.
+tpred1h*     | Length of time window in seconds in which the waveform is predicted using the AR model.                                      |
-*checkwinS* | Time in front of the global maximum of the ARH-CF in which to search for a second local extrema.
+| *
-*minfactorP* | Used with *checkwinS*. If a second local maximum is found, it has to be at least as big as the first maximum * *minfactorS*.
+AICtsmoothS* | Average of samples in this time window is used for smoothing the AIC-CF.                                                     |
-*tsignal* | Time window in seconds after the initial P pick used for determining signal amplitude.
+| *
-*tnoise* | Time window in seconds in front of initial P pick used for determining noise amplitude.
+pickwinS*    | Time window in which the minimum in the AIC-CF in front of the maximum in the ARH-CF is determined.                          |
-*tsafetey* | Time in seconds between *tsignal* and *tnoise*.
+| *
-*tslope* | Time window in seconds after initial P pick in which the slope of the onset is calculated.
+checkwinS*   | Time in front of the global maximum of the ARH-CF in which to search for a second local extrema.                             |
+| *minfactorP*  | Used with *
+checkwinS*. If a second local maximum is found, it has to be at least as big as the first maximum * *minfactorS*. |
+| *
+tsignal*     | Time window in seconds after the initial P pick used for determining signal amplitude.                                       |
+| *
+tnoise*      | Time window in seconds in front of initial P pick used for determining noise amplitude.                                      |
+| *tsafetey*    | Time in seconds between *tsignal* and *
+tnoise*.                                                                              |
+| *
+tslope*      | Time window in seconds after initial P pick in which the slope of the onset is calculated.                                   |
 
 ## Precise P pick
 
 Parameters used for determination of precise P pick.
 
-Name | Description
+| Name         | Description                                                                                                                                                                                                             |
---- | ---
+|--------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-*Precalcwin* | Time window in seconds for recalculation of the HOS-CF. The new CF will be two times the size of *Precalcwin*, since it will be calculated from the initial pick to +/- *Precalcwin*.
+| *Precalcwin* | Time window in seconds for recalculation of the HOS-CF. The new CF will be two times the size of *
-*tsmoothP* | Average of samples in this time window will be used for smoothing the second HOS-CF.
+Precalcwin*, since it will be calculated from the initial pick to +/- *Precalcwin*.                                   |
-*ausP* | Controls artificial uplift of samples during precise picking. A common local minimum of the smoothed and unsmoothed HOS-CF is found when the previous sample is larger or equal to the current sample times (1+*ausP*).
+| *
+tsmoothP*   | Average of samples in this time window will be used for smoothing the second HOS-CF.                                                                                                                                    |
+| *
+ausP*       | Controls artificial uplift of samples during precise picking. A common local minimum of the smoothed and unsmoothed HOS-CF is found when the previous sample is larger or equal to the current sample times (1+*
+ausP*). |
 
 ## Precise S pick
 
 Parameters used for determination of precise S pick.
 
-Name | Description
+| Name         | Description                                                                                                                                                                                                             |
---- | ---
+|--------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-*tdet2h* | Time window for determination of AR coefficients.
+| *
-*tpred2h* | Time window in which the waveform is predicted using the determined AR parameters.
+tdet2h*     | Time window for determination of AR coefficients.                                                                                                                                                                       |
-*Srecalcwin* | Time window for recalculation of ARH-CF. New CF will be calculated from initial pick +/- *Srecalcwin*.
+| *
-*tsmoothS* | Average of samples in this time window will be used for smoothing the second ARH-CF.
+tpred2h*    | Time window in which the waveform is predicted using the determined AR parameters.                                                                                                                                      |
-*ausS* | Controls artificial uplift of samples during precise picking. A common local minimum of the smoothed and unsmoothed ARH-CF is found when the previous sample is larger or equal to the current sample times (1+*ausS*). 
+| *Srecalcwin* | Time window for recalculation of ARH-CF. New CF will be calculated from initial pick +/- *
-*pickwinS* | Time window around initial pick in which to look for a precise pick.
+Srecalcwin*.                                                                                                                  |
-
+| *
+tsmoothS*   | Average of samples in this time window will be used for smoothing the second ARH-CF.                                                                                                                                    |
+| *
+ausS*       | Controls artificial uplift of samples during precise picking. A common local minimum of the smoothed and unsmoothed ARH-CF is found when the previous sample is larger or equal to the current sample times (1+*
+ausS*). |
+| *
+pickwinS*   | Time window around initial pick in which to look for a precise pick.                                                                                                                                                    |
 
 ## Pick quality control
 
 Parameters used for checking quality and integrity of automatic picks.
 
-Name | Description
+| Name                                       | Description                                                                                                                                                                                                                                                                    |
---- | ---
+|--------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-*minAICPslope* | Initial P picks with a slope lower than this value will be discared.
+| *
-*minAICPSNR* | Initial P picks with a SNR below this value will be discarded.
+minAICPslope*                             | Initial P picks with a slope lower than this value will be discared.                                                                                                                                                                                                           |
-*minAICSslope* | Initial S picks with a slope lower than this value will be discarded.
+| *
-*minAICSSNR* | Initial S picks with a SNR below this value will be discarded.
+minAICPSNR*                               | Initial P picks with a SNR below this value will be discarded.                                                                                                                                                                                                                 |
-*minsiglength*, *noisefacor*. *minpercent* | Parameters for checking signal length. In the time window of size *minsiglength* after the initial P pick *minpercent* of samples have to be larger than the RMS value.
+| *
-*zfac* | To recognize misattributed S picks, the RMS amplitude of vertical and horizontal traces are compared. The RMS amplitude of the vertical traces has to be at least *zfac* higher than the RMS amplitude on the horizontal traces for the pick to be accepted as a valid P pick.
+minAICSslope*                             | Initial S picks with a slope lower than this value will be discarded.                                                                                                                                                                                                          |
-*jackfactor* | A P pick is removed if the jackknife pseudo value of the variance of his subgroup is larger than the variance of all picks multiplied with the *jackfactor*.
+| *
-*mdttolerance* | Maximum allowed deviation of P onset times from the median. Value in seconds.
+minAICSSNR*                               | Initial S picks with a SNR below this value will be discarded.                                                                                                                                                                                                                 |
-*wdttolerance* | Maximum allowed deviation of S onset times from the line during the Wadati test. Value in seconds.
+| *minsiglength*, *noisefacor*. *minpercent* | Parameters for checking signal length. In the time window of size *
+minsiglength* after the initial P pick *
+minpercent* of samples have to be larger than the RMS value.                                                                                                        |
+| *
+zfac*                                     | To recognize misattributed S picks, the RMS amplitude of vertical and horizontal traces are compared. The RMS amplitude of the vertical traces has to be at least *
+zfac* higher than the RMS amplitude on the horizontal traces for the pick to be accepted as a valid P pick. |
+| *
+jackfactor*                               | A P pick is removed if the jackknife pseudo value of the variance of his subgroup is larger than the variance of all picks multiplied with the *
+jackfactor*.                                                                                                                   |
+| *
+mdttolerance*                             | Maximum allowed deviation of P onset times from the median. Value in seconds.                                                                                                                                                                                                  |
+| *
+wdttolerance*                             | Maximum allowed deviation of S onset times from the line during the Wadati test. Value in seconds.                                                                                                                                                                             |
 
 ## Pick quality determination
 
 Parameters for discrete quality classes.
 
-Name | Description
+| Name             | Description                                                                                                                                                                                                           |
---- | ---
+|------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-*timeerrorsP* | Width of the time windows in seconds between earliest and latest possible pick which represent the quality classes 0, 1, 2, 3 for P onsets.
+| *
-*timeerrorsS* |  Width of the time windows in seconds between earliest and latest possible pick which represent the quality classes 0, 1, 2, 3 for S onsets.
+timeerrorsP*    | Width of the time windows in seconds between earliest and latest possible pick which represent the quality classes 0, 1, 2, 3 for P onsets.                                                                           |
-*nfacP*, *nfacS* | For determination of latest possible onset time. The time when the signal reaches an amplitude of *nfac* * mean value of the RMS amplitude in the time window *tnoise* corresponds to the latest possible onset time.
+| *
+timeerrorsS*    | Width of the time windows in seconds between earliest and latest possible pick which represent the quality classes 0, 1, 2, 3 for S onsets.                                                                           |
+| *nfacP*, *nfacS* | For determination of latest possible onset time. The time when the signal reaches an amplitude of *
+nfac* * mean value of the RMS amplitude in the time window *tnoise* corresponds to the latest possible onset time. |
 

pylot.yml

@@ -1,118 +1,14 @@
-name: pylot_py35
+name: pylot_38
 channels:
   - conda-forge
   - defaults
 dependencies:
-  - _libgcc_mutex=0.1=conda_forge
+  - cartopy=0.20.2
-  - _openmp_mutex=4.5=1_gnu
+  - matplotlib-base=3.3.4
-  - brotlipy=0.7.0=py36h8f6f2f9_1001
+  - numpy=1.22.3
-  - c-ares=1.17.1=h7f98852_1
+  - obspy=1.3.0
-  - ca-certificates=2021.5.30=ha878542_0
+  - pyqtgraph=0.12.4
-  - cartopy=0.18.0=py36h104b3a8_13
+  - pyside2>=5.13.2
-  - certifi=2021.5.30=py36h5fab9bb_0
+  - python=3.8.12
-  - cffi=1.14.5=py36hc120d54_0
+  - qt>=5.12.9
-  - chardet=4.0.0=py36h5fab9bb_1
+  - scipy=1.8.0
-  - cryptography=3.4.6=py36hb60f036_0
-  - cycler=0.10.0=py_2
-  - dbus=1.13.6=hfdff14a_1
-  - decorator=4.4.2=py_0
-  - expat=2.2.10=h9c3ff4c_0
-  - fontconfig=2.13.1=hba837de_1004
-  - freetype=2.10.4=h0708190_1
-  - future=0.18.2=py36h5fab9bb_3
-  - geos=3.9.1=h9c3ff4c_2
-  - gettext=0.19.8.1=h0b5b191_1005
-  - glib=2.68.0=h9c3ff4c_1
-  - glib-tools=2.68.0=h9c3ff4c_1
-  - greenlet=1.0.0=py36hc4f0c31_0
-  - gst-plugins-base=1.18.4=h29181c9_0
-  - gstreamer=1.18.4=h76c114f_0
-  - icu=68.1=h58526e2_0
-  - idna=2.10=pyh9f0ad1d_0
-  - importlib-metadata=3.7.3=py36h5fab9bb_0
-  - jpeg=9d=h36c2ea0_0
-  - kiwisolver=1.3.1=py36h605e78d_1
-  - krb5=1.17.2=h926e7f8_0
-  - lcms2=2.12=hddcbb42_0
-  - ld_impl_linux-64=2.35.1=hea4e1c9_2
-  - libblas=3.9.0=8_openblas
-  - libcblas=3.9.0=8_openblas
-  - libclang=11.1.0=default_ha53f305_0
-  - libcurl=7.75.0=hc4aaa36_0
-  - libedit=3.1.20191231=he28a2e2_2
-  - libev=4.33=h516909a_1
-  - libevent=2.1.10=hcdb4288_3
-  - libffi=3.3=h58526e2_2
-  - libgcc-ng=9.3.0=h2828fa1_18
-  - libgfortran-ng=9.3.0=hff62375_18
-  - libgfortran5=9.3.0=hff62375_18
-  - libglib=2.68.0=h3e27bee_1
-  - libgomp=9.3.0=h2828fa1_18
-  - libiconv=1.16=h516909a_0
-  - liblapack=3.9.0=8_openblas
-  - libllvm11=11.1.0=hf817b99_0
-  - libnghttp2=1.43.0=h812cca2_0
-  - libopenblas=0.3.12=pthreads_h4812303_1
-  - libpng=1.6.37=h21135ba_2
-  - libpq=13.1=hfd2b0eb_2
-  - libssh2=1.9.0=ha56f1ee_6
-  - libstdcxx-ng=9.3.0=h6de172a_18
-  - libtiff=4.2.0=hdc55705_0
-  - libuuid=2.32.1=h7f98852_1000
-  - libwebp-base=1.2.0=h7f98852_2
-  - libxcb=1.13=h7f98852_1003
-  - libxkbcommon=1.0.3=he3ba5ed_0
-  - libxml2=2.9.10=h72842e0_3
-  - libxslt=1.1.33=h15afd5d_2
-  - lxml=4.6.2=py36h04a5ba7_1
-  - lz4-c=1.9.3=h9c3ff4c_0
-  - matplotlib-base=3.3.4=py36hd391965_0
-  - mysql-common=8.0.23=ha770c72_1
-  - mysql-libs=8.0.23=h935591d_1
-  - ncurses=6.2=h58526e2_4
-  - nspr=4.30=h9c3ff4c_0
-  - nss=3.63=hb5efdd6_0
-  - numpy=1.19.5=py36h2aa4a07_1
-  - obspy=1.2.2=py36h785e9b2_0
-  - olefile=0.46=pyh9f0ad1d_1
-  - openssl=1.1.1k=h7f98852_0
-  - pandas=1.1.5=py36h284efc9_0
-  - pcre=8.44=he1b5a44_0
-  - pillow=8.1.2=py36ha6010c0_0
-  - pip=21.0.1=pyhd8ed1ab_0
-  - proj=7.2.0=h277dcde_2
-  - pthread-stubs=0.4=h36c2ea0_1001
-  - pycparser=2.20=pyh9f0ad1d_2
-  - pyopenssl=20.0.1=pyhd8ed1ab_0
-  - pyparsing=2.4.7=pyh9f0ad1d_0
-  - pyqt5-sip=4.19.18=py36hc4f0c31_7
-  - pyqtgraph=0.11.1=pyhd3deb0d_0
-  - pyshp=2.1.3=pyh44b312d_0
-  - pyside2=5.13.2=py36h6b97533_4
-  - pysocks=1.7.1=py36h5fab9bb_3
-  - python=3.6.13=hffdb5ce_0_cpython
-  - python-dateutil=2.8.1=py_0
-  - python_abi=3.6=1_cp36m
-  - pytz=2021.1=pyhd8ed1ab_0
-  - qt=5.12.9=hda022c4_4
-  - qtpy=1.9.0=py_0
-  - readline=8.0=he28a2e2_2
-  - requests=2.25.1=pyhd3deb0d_0
-  - scipy=1.5.3=py36h9e8f40b_0
-  - setuptools=49.6.0=py36h5fab9bb_3
-  - shapely=1.7.1=py36h93b233e_4
-  - six=1.15.0=pyh9f0ad1d_0
-  - sqlalchemy=1.4.2=py36h8f6f2f9_0
-  - sqlite=3.34.0=h74cdb3f_0
-  - tk=8.6.10=h21135ba_1
-  - tornado=6.1=py36h8f6f2f9_1
-  - typing_extensions=3.7.4.3=py_0
-  - urllib3=1.26.4=pyhd8ed1ab_0
-  - wheel=0.36.2=pyhd3deb0d_0
-  - xorg-libxau=1.0.9=h7f98852_0
-  - xorg-libxdmcp=1.1.3=h7f98852_0
-  - xz=5.2.5=h516909a_1
-  - zipp=3.4.1=pyhd8ed1ab_0
-  - zlib=1.2.11=h516909a_1010
-  - zstd=1.4.9=ha95c52a_0
-prefix:

pylot/core/analysis/magnitude.py

@@ -144,6 +144,10 @@ class Magnitude(object):
                     azimuthal_gap=self.origin_id.get_referred_object().quality.azimuthal_gap)
             else:
                 # no scaling necessary
+                # Temporary fix needs rework
+                if (len(self.magnitudes.keys()) == 0):
+                    print("Error in local magnitude calculation ")
+                    return None
                 mag = ope.Magnitude(
                     mag=np.median([M.mag for M in self.magnitudes.values()]),
                     magnitude_type=self.type,
@@ -223,13 +227,13 @@ class LocalMagnitude(Magnitude):
                  in 'Z3']
         # checking horizontal count and calculating power_sum accordingly
         if len(power) == 1:
-                print ('WARNING: Only one horizontal found for station {0}.'.format(st[0].stats.station))
+            print('WARNING: Only one horizontal found for station {0}.'.format(st[0].stats.station))
-                power_sum = power[0]
+            power_sum = power[0]
         elif len(power) == 2:
-                power_sum = power[0] + power[1]
+            power_sum = power[0] + power[1]
         else:
-                raise ValueError('Wood-Anderson aomplitude defintion only valid for'
+            raise ValueError('Wood-Anderson aomplitude defintion only valid for'
-                        ' up to two horizontals: {0} given'.format(len(power)))
+                             ' up to two horizontals: {0} given'.format(len(power)))
 
         sqH = np.sqrt(power_sum)
 
@@ -325,7 +329,7 @@ class LocalMagnitude(Magnitude):
             if self.verbose:
                 print(
                     "Local Magnitude for station {0}: ML = {1:3.1f}".format(
-                       station, magnitude.mag))
+                        station, magnitude.mag))
             magnitude.origin_id = self.origin_id
             magnitude.waveform_id = pick.waveform_id
             magnitude.amplitude_id = amplitude.resource_id
@@ -404,7 +408,7 @@ class MomentMagnitude(Magnitude):
             if not wf:
                 continue
             try:
-               scopy = wf.copy()
+                scopy = wf.copy()
             except AssertionError:
                 print("WARNING: Something's wrong with the data,"
                       "station {},"
@@ -414,6 +418,10 @@ class MomentMagnitude(Magnitude):
             distance = degrees2kilometers(a.distance)
             azimuth = a.azimuth
             incidence = a.takeoff_angle
+            if not 0. <= incidence <= 360.:
+                if self.verbose:
+                    print(f'WARNING: Incidence angle outside bounds - {incidence}')
+                return
             w0, fc = calcsourcespec(scopy, onset, self.p_velocity, distance,
                                     azimuth, incidence, self.p_attenuation,
                                     self.plot_flag, self.verbose)
@@ -432,6 +440,40 @@ class MomentMagnitude(Magnitude):
             self.event.station_magnitudes.append(magnitude)
             self.magnitudes = (station, magnitude)
 
+    # WIP JG
+    def getSourceSpec(self):
+        for a in self.arrivals:
+            if a.phase not in 'pP':
+                continue
+            # make sure calculating Mo only from reliable onsets
+            # NLLoc: time_weight = 0 => do not use onset!
+            if a.time_weight == 0:
+                continue
+            pick = a.pick_id.get_referred_object()
+            station = pick.waveform_id.station_code
+            if len(self.stream) <= 2:
+                print("Station:" '{0}'.format(station))
+                print("WARNING: No instrument corrected data available,"
+                      " no magnitude calculation possible! Go on.")
+                continue
+            wf = self.stream.select(station=station)
+            if not wf:
+                continue
+            try:
+                scopy = wf.copy()
+            except AssertionError:
+                print("WARNING: Something's wrong with the data,"
+                      "station {},"
+                      "no calculation of moment magnitude possible! Go on.".format(station))
+                continue
+            onset = pick.time
+            distance = degrees2kilometers(a.distance)
+            azimuth = a.azimuth
+            incidence = a.takeoff_angle
+            w0, fc, plt = calcsourcespec(scopy, onset, self.p_velocity, distance,
+                                    azimuth, incidence, self.p_attenuation,
+                                    3, self.verbose)
+            return w0, fc, plt
 
 def calcMoMw(wfstream, w0, rho, vp, delta, verbosity=False):
     '''
@@ -594,15 +636,15 @@ def calcsourcespec(wfstream, onset, vp, delta, azimuth, incidence,
 
         # fft
         fny = freq / 2
-        #l = len(xdat) / freq
+        # l = len(xdat) / freq
         # number of fft bins after Bath
-        #n = freq * l
+        # n = freq * l
         # find next power of 2 of data length
         m = pow(2, np.ceil(np.log(len(xdat)) / np.log(2)))
         N = min(int(np.power(m, 2)), 16384)
-        #N = int(np.power(m, 2))
+        # N = int(np.power(m, 2))
         y = dt * np.fft.fft(xdat, N)
-        Y = abs(y[: N / 2])
+        Y = abs(y[: int(N / 2)])
         L = (N - 1) / freq
         f = np.arange(0, fny, 1 / L)
 
@@ -643,8 +685,8 @@ def calcsourcespec(wfstream, onset, vp, delta, azimuth, incidence,
         w0 = np.median([w01, w02])
         Fc = np.median([fc1, fc2])
         if verbosity:
-           print("calcsourcespec: Using w0-value = %e m/Hz and fc = %f Hz" % (
+            print("calcsourcespec: Using w0-value = %e m/Hz and fc = %f Hz" % (
-              w0, Fc))
+                w0, Fc))
     if iplot >= 1:
         f1 = plt.figure()
         tLdat = np.arange(0, len(Ldat) * dt, dt)
@@ -679,6 +721,8 @@ def calcsourcespec(wfstream, onset, vp, delta, azimuth, incidence,
             plt.xlabel('Frequency [Hz]')
             plt.ylabel('Amplitude [m/Hz]')
             plt.grid()
+            if iplot == 3:
+                return w0, Fc, plt
             plt.show()
             try:
                 input()

pylot/core/io/data.py

@@ -3,16 +3,16 @@
 
 import copy
 import os
+
+from PySide2.QtWidgets import QMessageBox
 from obspy import read_events
 from obspy.core import read, Stream, UTCDateTime
 from obspy.core.event import Event as ObsPyEvent
 from obspy.io.sac import SacIOError
 
-from PySide2.QtWidgets import QMessageBox
-
-import pylot.core.loc.velest as velest
 import pylot.core.loc.focmec as focmec
 import pylot.core.loc.hypodd as hypodd
+import pylot.core.loc.velest as velest
 from pylot.core.io.phases import readPILOTEvent, picks_from_picksdict, \
     picksdict_from_pilot, merge_picks, PylotParameter
 from pylot.core.util.errors import FormatError, OverwriteError
@@ -100,7 +100,7 @@ class Data(object):
                                       old_pick.phase_hint == new_pick.phase_hint,
                                       old_pick.method_id == new_pick.method_id]
                         if all(comparison):
-                            del(old_pick)
+                            del (old_pick)
                 old_picks.append(new_pick)
         elif not other.isNew() and self.isNew():
             new = other + self
@@ -112,7 +112,7 @@ class Data(object):
             return self + other
         else:
             raise ValueError("both Data objects have differing "
-              "unique Event identifiers")
+                             "unique Event identifiers")
         return self
 
     def getPicksStr(self):
@@ -260,7 +260,6 @@ class Data(object):
         can be a str or a list of strings of ['manual', 'auto', 'origin', 'magnitude']
         """
         from pylot.core.util.defaults import OUTPUTFORMATS
-        
         if not type(fcheck) == list:
             fcheck = [fcheck]
 
@@ -321,35 +320,61 @@ class Data(object):
             if lendiff != 0:
                 print("Manual as well as automatic picks available. Prefered the {} manual ones!".format(lendiff))
 
+
+            no_uncertainties_p = []
+            no_uncertainties_s = []
             if upperErrors:
                 # check for pick uncertainties exceeding adjusted upper errors
                 # Picks with larger uncertainties will not be saved in output file!
                 for j in range(len(picks)):
                     for i in range(len(picks_copy)):
                         if picks_copy[i].phase_hint[0] == 'P':
-                            if (picks_copy[i].time_errors['upper_uncertainty'] >= upperErrors[0]) or \
+                            # Skipping pick if no upper_uncertainty is found and warning user
-                                    (picks_copy[i].time_errors['uncertainty'] is None):
+                            if picks_copy[i].time_errors['upper_uncertainty'] is None:
+                                #print("{1} P-Pick of station {0} does not have upper_uncertainty and cant be checked".format(
+                                 #   picks_copy[i].waveform_id.station_code,
+                                  #  picks_copy[i].method_id))
+                                if not picks_copy[i].waveform_id.station_code in no_uncertainties_p:
+                                    no_uncertainties_p.append(picks_copy[i].waveform_id.station_code)
+                                continue
+
+                            #print ("checking for upper_uncertainty")
+                            if  (picks_copy[i].time_errors['uncertainty'] is None) or \
+                                    (picks_copy[i].time_errors['upper_uncertainty'] >= upperErrors[0]):
                                 print("Uncertainty exceeds or equal adjusted upper time error!")
                                 print("Adjusted uncertainty: {}".format(upperErrors[0]))
                                 print("Pick uncertainty: {}".format(picks_copy[i].time_errors['uncertainty']))
                                 print("{1} P-Pick of station {0} will not be saved in outputfile".format(
                                     picks_copy[i].waveform_id.station_code,
                                     picks_copy[i].method_id))
-                                print("#")
                                 del picks_copy[i]
                                 break
                         if picks_copy[i].phase_hint[0] == 'S':
-                            if (picks_copy[i].time_errors['upper_uncertainty'] >= upperErrors[1]) or \
+
-                                    (picks_copy[i].time_errors['uncertainty'] is None):
+                            # Skipping pick if no upper_uncertainty is found and warning user
+                            if picks_copy[i].time_errors['upper_uncertainty'] is None:
+                                #print("{1} S-Pick of station {0} does not have upper_uncertainty and cant be checked".format(
+                                    #picks_copy[i].waveform_id.station_code,
+                                    #picks_copy[i].method_id))
+                                if not picks_copy[i].waveform_id.station_code in no_uncertainties_s:
+                                    no_uncertainties_s.append(picks_copy[i].waveform_id.station_code)
+                                continue
+
+
+                            if (picks_copy[i].time_errors['uncertainty'] is None) or \
+                                (picks_copy[i].time_errors['upper_uncertainty'] >= upperErrors[1]):
                                 print("Uncertainty exceeds or equal adjusted upper time error!")
                                 print("Adjusted uncertainty: {}".format(upperErrors[1]))
                                 print("Pick uncertainty: {}".format(picks_copy[i].time_errors['uncertainty']))
                                 print("{1} S-Pick of station {0} will not be saved in outputfile".format(
                                     picks_copy[i].waveform_id.station_code,
                                     picks_copy[i].method_id))
-                                print("#")
                                 del picks_copy[i]
                                 break
+            for s in no_uncertainties_p:
+                print("P-Pick of station {0} does not have upper_uncertainty and cant be checked".format(s))
+            for s in no_uncertainties_s:
+                print("S-Pick of station {0} does not have upper_uncertainty and cant be checked".format(s))
 
             if fnext == '.obs':
                 try:
@@ -360,13 +385,13 @@ class Data(object):
                     nllocfile = open(fnout + fnext)
                     l = nllocfile.readlines()
                     # Adding A0/Generic Amplitude to .obs file
-                    #l2 = []
+                    # l2 = []
-                    #for li in l:
+                    # for li in l:
                     #    for amp in evtdata_org.amplitudes:
                     #        if amp.waveform_id.station_code == li[0:5].strip():
                     #            li = li[0:64] + '{:0.2e}'.format(amp.generic_amplitude) + li[73:-1] + '\n'
                     #            l2.append(li)
-                    #l = l2
+                    # l = l2
                     nllocfile.close()
                     l.insert(0, header)
                     nllocfile = open(fnout + fnext, 'w')
@@ -426,20 +451,25 @@ class Data(object):
         data.filter(**kwargs)
         self.dirty = True
 
-    def setWFData(self, fnames, fnames_syn=None, checkRotated=False, metadata=None, tstart=0, tstop=0):
+    def setWFData(self, fnames, fnames_alt=None, checkRotated=False, metadata=None, tstart=0, tstop=0):
         """
         Clear current waveform data and set given waveform data
         :param fnames: waveform data names to append
+        :param fnames_alt: alternative data to show (e.g. synthetic/processed)
         :type fnames: list
         """
         self.wfdata = Stream()
         self.wforiginal = None
-        self.wfsyn = Stream()
+        self.wf_alt = Stream()
         if tstart == tstop:
             tstart = tstop = None
         self.tstart = tstart
         self.tstop = tstop
 
+        # remove directories
+        fnames = [fname for fname in fnames if not os.path.isdir(fname)]
+        fnames_alt = [fname for fname in fnames_alt if not os.path.isdir(fname)]
+
         # if obspy_dmt:
         #     wfdir = 'raw'
         #     self.processed = False
@@ -457,8 +487,8 @@ class Data(object):
         #     wffnames = fnames
         if fnames is not None:
             self.appendWFData(fnames)
-            if fnames_syn is not None:
+            if fnames_alt is not None:
-                self.appendWFData(fnames_syn, synthetic=True)
+                self.appendWFData(fnames_alt, alternative=True)
         else:
             return False
 
@@ -478,7 +508,7 @@ class Data(object):
         self.dirty = False
         return True
 
-    def appendWFData(self, fnames, synthetic=False):
+    def appendWFData(self, fnames, alternative=False):
         """
         Read waveform data from fnames and append it to current wf data
         :param fnames: waveform data to append
@@ -491,19 +521,20 @@ class Data(object):
         if self.dirty:
             self.resetWFData()
 
-        real_or_syn_data = {True: self.wfsyn,
+        orig_or_alternative_data = {True: self.wf_alt,
-                            False: self.wfdata}
+                                    False: self.wfdata}
 
         warnmsg = ''
         for fname in set(fnames):
             try:
-                real_or_syn_data[synthetic] += read(fname, starttime=self.tstart, endtime=self.tstop)
+                orig_or_alternative_data[alternative] += read(fname, starttime=self.tstart, endtime=self.tstop)
             except TypeError:
                 try:
-                    real_or_syn_data[synthetic] += read(fname, format='GSE2', starttime=self.tstart, endtime=self.tstop)
+                    orig_or_alternative_data[alternative] += read(fname, format='GSE2', starttime=self.tstart, endtime=self.tstop)
                 except Exception as e:
                     try:
-                        real_or_syn_data[synthetic] += read(fname, format='SEGY', starttime=self.tstart, endtime=self.tstop)
+                        orig_or_alternative_data[alternative] += read(fname, format='SEGY', starttime=self.tstart,
+                                                                      endtime=self.tstop)
                     except Exception as e:
                         warnmsg += '{0}\n{1}\n'.format(fname, e)
             except SacIOError as se:
@@ -518,8 +549,8 @@ class Data(object):
     def getOriginalWFData(self):
         return self.wforiginal
 
-    def getSynWFData(self):
+    def getAltWFdata(self):
-        return self.wfsyn
+        return self.wf_alt
 
     def resetWFData(self):
         """

pylot/core/io/default_parameters.py

@@ -515,9 +515,9 @@ defaults = {'rootpath': {'type': str,
                            'namestring': 'TauPy model'},
 
             'taup_phases': {'type': str,
-                           'tooltip': 'Specify possible phases for TauPy (comma separated). See Obspy TauPy documentation for possible values.',
+                            'tooltip': 'Specify possible phases for TauPy (comma separated). See Obspy TauPy documentation for possible values.',
-                           'value': 'ttall',
+                            'value': 'ttall',
-                           'namestring': 'TauPy phases'},
+                            'namestring': 'TauPy phases'},
             }
 
 settings_main = {

pylot/core/io/getEventListFromXML.py

@@ -8,14 +8,15 @@
    Edited for use in PyLoT
    JG, igem, 01/2022
 """
-import pdb
+
 import os
 import argparse
 import numpy as np
 import matplotlib.pyplot as plt
+import glob
+
 from obspy.core.event import read_events
 from pyproj import Proj
-import glob
 
 """
 Creates an eventlist file summarizing all events found in a certain folder. Only called by pressing UI Button eventlis_xml_action
@@ -24,14 +25,15 @@ Creates an eventlist file summarizing all events found in a certain folder. Only
 :param path: Path to root folder where single Event folder are to found
 """
 
+
 def geteventlistfromxml(path, outpath):
     p = Proj(proj='utm', zone=32, ellps='WGS84')
 
-
     # open eventlist file and write header
     evlist = outpath + '/eventlist'
     evlistobj = open(evlist, 'w')
-    evlistobj.write('EventID      Date      To    Lat    Lon     EAST    NORTH  Dep  Ml  NoP  NoS RMS  errH  errZ  Gap \n')
+    evlistobj.write(
+        'EventID      Date      To    Lat    Lon     EAST    NORTH  Dep  Ml  NoP  NoS RMS  errH  errZ  Gap \n')
 
     # data path
     dp = path + "/e*/*.xml"
@@ -52,7 +54,8 @@ def geteventlistfromxml(path, outpath):
             NoP = []
             NoS = []
         except IndexError:
-            print ('Insufficient data found for event (not localised): ' + names.split('/')[-1].split('_')[-1][:-4] + ' Skipping event for eventlist.' )
+            print('Insufficient data found for event (not localised): ' + names.split('/')[-1].split('_')[-1][
+                                                                          :-4] + ' Skipping event for eventlist.')
             continue
 
         for i in range(len(cat.events[0].origins[0].arrivals)):
@@ -60,20 +63,22 @@ def geteventlistfromxml(path, outpath):
                 NoP.append(cat.events[0].origins[0].arrivals[i].phase)
             elif cat.events[0].origins[0].arrivals[i].phase == 'S':
                 NoS.append(cat.events[0].origins[0].arrivals[i].phase)
-        #NoP = cat.events[0].origins[0].quality.used_station_count
+        # NoP = cat.events[0].origins[0].quality.used_station_count
         errH = cat.events[0].origins[0].origin_uncertainty.max_horizontal_uncertainty
         errZ = cat.events[0].origins[0].depth_errors.uncertainty
         Gap = cat.events[0].origins[0].quality.azimuthal_gap
-        #evID = names.split('/')[6]
+        # evID = names.split('/')[6]
         evID = names.split('/')[-1].split('_')[-1][:-4]
         Date = str(st.year) + str('%02d' % st.month) + str('%02d' % st.day)
         To = str('%02d' % st.hour) + str('%02d' % st.minute) + str('%02d' % st.second) + \
-                  '.' + str('%06d' % st.microsecond)
+             '.' + str('%06d' % st.microsecond)
 
         # write into eventlist
-        evlistobj.write('%s %s %s %9.6f %9.6f %13.6f %13.6f %8.6f %3.1f %d %d NaN %d %d %d\n' %(evID, \
+        evlistobj.write('%s %s %s %9.6f %9.6f %13.6f %13.6f %8.6f %3.1f %d %d NaN %d %d %d\n' % (evID, \
-                         Date, To, Lat, Lon, EAST, NORTH, Dep, Ml, len(NoP), len(NoS), errH, errZ, Gap))
+                                                                                                 Date, To, Lat, Lon,
-        print ('Adding Event ' + names.split('/')[-1].split('_')[-1][:-4]  + ' to eventlist')
+                                                                                                 EAST, NORTH, Dep, Ml,
+                                                                                                 len(NoP), len(NoS),
+                                                                                                 errH, errZ, Gap))
+        print('Adding Event ' + names.split('/')[-1].split('_')[-1][:-4] + ' to eventlist')
     print('Eventlist created and saved in: ' + outpath)
     evlistobj.close()
-

pylot/core/io/getQualitiesfromxml.py

@@ -1,139 +0,0 @@
-#!/usr/bin/python
-# -*- coding: utf-8 -*-
-
-"""
-   Script to get onset uncertainties from Quakeml.xml files created by PyLoT.
-   Uncertainties are tranformed into quality classes and visualized via histogram if desired.
-   Ludger Küperkoch, BESTEC GmbH, 07/2017
-   rev.: Ludger Küperkoch, igem, 10/2020
-   Edited for usage in PyLoT: Jeldrik Gaal, igem, 01/2022
-"""
-
-import argparse
-import numpy as np
-import matplotlib.pyplot as plt
-from obspy.core.event import read_events
-import glob
-
-def getQualitiesfromxml(path):
-
-    # uncertainties 
-    ErrorsP = [0.02, 0.04, 0.08, 0.16]
-    ErrorsS = [0.04, 0.08, 0.16, 0.32]
-
-    Pw0 = []
-    Pw1 = []
-    Pw2 = []
-    Pw3 = []
-    Pw4 = []
-    Sw0 = []
-    Sw1 = []
-    Sw2 = []
-    Sw3 = []
-    Sw4 = []
-    
-    # data path
-    dp = path + '/e*/*.xml'
-    # list of all available xml-files
-    xmlnames = glob.glob(dp)
-
-    # read all onset weights
-    for names in xmlnames: 
-        print("Getting onset weights from {}".format(names))
-        cat = read_events(names)
-        arrivals = cat.events[0].picks
-        for Pick in arrivals:
-            if Pick.phase_hint[0] == 'P':
-                if Pick.time_errors.uncertainty <= ErrorsP[0]:     
-                    Pw0.append(Pick.time_errors.uncertainty)      
-                elif Pick.time_errors.uncertainty > ErrorsP[0] and \
-                     Pick.time_errors.uncertainty <= ErrorsP[1]:
-                     Pw1.append(Pick.time_errors.uncertainty)      
-                elif Pick.time_errors.uncertainty > ErrorsP[1] and \
-                     Pick.time_errors.uncertainty <= ErrorsP[2]:
-                     Pw2.append(Pick.time_errors.uncertainty)      
-                elif Pick.time_errors.uncertainty > ErrorsP[2] and \
-                     Pick.time_errors.uncertainty <= ErrorsP[3]:
-                     Pw3.append(Pick.time_errors.uncertainty)      
-                elif Pick.time_errors.uncertainty > ErrorsP[3]:
-                     Pw4.append(Pick.time_errors.uncertainty)      
-                else:
-                    pass
-            elif Pick.phase_hint[0] == 'S':
-                if Pick.time_errors.uncertainty <= ErrorsS[0]:     
-                     Sw0.append(Pick.time_errors.uncertainty)      
-                elif Pick.time_errors.uncertainty > ErrorsS[0] and \
-                     Pick.time_errors.uncertainty <= ErrorsS[1]:
-                     Sw1.append(Pick.time_errors.uncertainty)      
-                elif Pick.time_errors.uncertainty > ErrorsS[1] and \
-                     Pick.time_errors.uncertainty <= ErrorsS[2]:
-                     Sw2.append(Pick.time_errors.uncertainty)      
-                elif Pick.time_errors.uncertainty > ErrorsS[2] and \
-                     Pick.time_errors.uncertainty <= ErrorsS[3]:
-                     Sw3.append(Pick.time_errors.uncertainty)      
-                elif Pick.time_errors.uncertainty > ErrorsS[3]:
-                     Sw4.append(Pick.time_errors.uncertainty)      
-                else:
-                    pass
-            else:
-                print("Phase hint not defined for picking!")
-                pass
-    # get percentage of weights
-    numPweights = np.sum([len(Pw0), len(Pw1), len(Pw2), len(Pw3), len(Pw4)])
-    numSweights = np.sum([len(Sw0), len(Sw1), len(Sw2), len(Sw3), len(Sw4)])
-    try:
-        P0perc = 100.0 / numPweights * len(Pw0)
-    except:
-        P0perc = 0
-    try:
-        P1perc = 100.0 / numPweights * len(Pw1)
-    except:
-        P1perc = 0
-    try:
-        P2perc = 100.0 / numPweights * len(Pw2)
-    except:
-        P2perc = 0
-    try:
-        P3perc = 100.0 / numPweights * len(Pw3)
-    except:
-        P3perc = 0
-    try:
-        P4perc = 100.0 / numPweights * len(Pw4)
-    except:
-        P4perc = 0
-    try:
-        S0perc = 100.0 / numSweights * len(Sw0)
-    except:
-        Soperc = 0
-    try:
-        S1perc = 100.0 / numSweights * len(Sw1)
-    except:
-        S1perc = 0
-    try:
-        S2perc = 100.0 / numSweights * len(Sw2)
-    except:
-        S2perc = 0
-    try:
-        S3perc = 100.0 / numSweights * len(Sw3)
-    except:
-        S3perc = 0
-    try:
-        S4perc = 100.0 / numSweights * len(Sw4)
-    except:
-        S4perc = 0
-
-    weights = ('0', '1', '2', '3', '4')
-    y_pos = np.arange(len(weights))
-    width = 0.34
-    p1, = plt.bar(0 - width, P0perc, width, color='black')
-    p2, = plt.bar(0, S0perc, width, color='red')
-    plt.bar(y_pos - width, [P0perc, P1perc, P2perc, P3perc, P4perc], width, color='black')
-    plt.bar(y_pos, [S0perc, S1perc, S2perc, S3perc, S4perc], width, color='red')
-    plt.ylabel('%')
-    plt.xticks(y_pos, weights)
-    plt.xlim([-0.5, 4.5])
-    plt.xlabel('Qualities')
-    plt.title('{0} P-Qualities, {1} S-Qualities'.format(numPweights, numSweights)) 
-    plt.legend([p1, p2], ['P-Weights', 'S-Weights'])
-    plt.show()
-

pylot/core/io/phases.py

@@ -1,13 +1,13 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
-import pdb
 import glob
+import os
+import warnings
+
 import matplotlib.pyplot as plt
 import numpy as np
 import obspy.core.event as ope
-import os
 import scipy.io as sio
-import warnings
 from obspy.core import UTCDateTime
 from obspy.core.event import read_events
 from obspy.core.util import AttribDict
@@ -17,7 +17,7 @@ from pylot.core.io.location import create_event, \
     create_magnitude
 from pylot.core.pick.utils import select_for_phase, get_quality_class
 from pylot.core.util.utils import getOwner, full_range, four_digits, transformFilterString4Export, \
-    backtransformFilterString
+    backtransformFilterString, loopIdentifyPhase, identifyPhase
 
 
 def add_amplitudes(event, amplitudes):
@@ -280,6 +280,7 @@ def picksdict_from_picks(evt):
             infile = os.path.join(os.path.expanduser('~'), '.pylot', 'pylot.in')
             print('Using default input file {}'.format(infile))
             parameter = PylotParameter(infile)
+            pick.phase_hint = identifyPhase(pick.phase_hint)
             if pick.phase_hint == 'P':
                 errors = parameter['timeerrorsP']
             elif pick.phase_hint == 'S':
@@ -375,7 +376,6 @@ def picks_from_picksdict(picks, creation_info=None):
 
 
 def reassess_pilot_db(root_dir, db_dir, out_dir=None, fn_param=None, verbosity=0):
-    import glob
     # TODO: change root to datapath
     db_root = os.path.join(root_dir, db_dir)
     evt_list = glob.glob1(db_root, 'e????.???.??')
@@ -393,9 +393,6 @@ def reassess_pilot_event(root_dir, db_dir, event_id, out_dir=None, fn_param=None
     from pylot.core.pick.utils import earllatepicker
     # TODO: change root to datapath
 
-    if fn_param is None:
-        fn_param = defaults.AUTOMATIC_DEFAULTS
-
     default = PylotParameter(fn_param, verbosity)
 
     search_base = os.path.join(root_dir, db_dir, event_id)
@@ -518,10 +515,10 @@ def writephases(arrivals, fformat, filename, parameter=None, eventinfo=None):
         # write header
         fid.write('# EQEVENT: %s Label: EQ%s  Loc:  X 0.00  Y 0.00  Z 10.00  OT 0.00 \n' %
                   (parameter.get('database'), parameter.get('eventID')))
-        arrivals = chooseArrivals(arrivals)
+        arrivals = chooseArrivals(arrivals)  # MP MP what is chooseArrivals? It is not defined anywhere
         for key in arrivals:
             # P onsets
-            if arrivals[key].has_key('P'):
+            if 'P' in arrivals[key]:
                 try:
                     fm = arrivals[key]['P']['fm']
                 except KeyError as e:
@@ -555,7 +552,7 @@ def writephases(arrivals, fformat, filename, parameter=None, eventinfo=None):
                                                                                            ss_ms,
                                                                                            pweight))
             # S onsets
-            if arrivals[key].has_key('S') and arrivals[key]['S']['mpp'] is not None:
+            if 'S' in arrivals[key] and arrivals[key]['S']['mpp'] is not None:
                 fm = '?'
                 onset = arrivals[key]['S']['mpp']
                 year = onset.year
@@ -572,20 +569,20 @@ def writephases(arrivals, fformat, filename, parameter=None, eventinfo=None):
                         sweight = 0  # do not use pick
                 except KeyError as e:
                     print(str(e) + '; no weight set during processing')
-                Ao = arrivals[key]['S']['Ao'] # peak-to-peak amplitude
+                Ao = arrivals[key]['S']['Ao']  # peak-to-peak amplitude
                 if Ao == None:
                     Ao = 0.0
-                #fid.write('%s ? ? ? S   %s %d%02d%02d %02d%02d %7.4f GAU 0 0 0 0 %d \n' % (key,
+                # fid.write('%s ? ? ? S   %s %d%02d%02d %02d%02d %7.4f GAU 0 0 0 0 %d \n' % (key,
                 fid.write('%s ? ? ? S   %s %d%02d%02d %02d%02d %7.4f GAU 0 %9.2f 0 0 %d \n' % (key,
-                                                                                           fm,
+                                                                                               fm,
-                                                                                           year,
+                                                                                               year,
-                                                                                           month,
+                                                                                               month,
-                                                                                           day,
+                                                                                               day,
-                                                                                           hh,
+                                                                                               hh,
-                                                                                           mm,
+                                                                                               mm,
-                                                                                           ss_ms,
+                                                                                               ss_ms,
-                                                                                           Ao,
+                                                                                               Ao,
-                                                                                           sweight))
+                                                                                               sweight))
 
         fid.close()
     elif fformat == 'HYPO71':
@@ -594,7 +591,7 @@ def writephases(arrivals, fformat, filename, parameter=None, eventinfo=None):
         # write header
         fid.write('                                                                %s\n' %
                   parameter.get('eventID'))
-        arrivals = chooseArrivals(arrivals)
+        arrivals = chooseArrivals(arrivals)  # MP MP what is chooseArrivals? It is not defined anywhere
         for key in arrivals:
             if arrivals[key]['P']['weight'] < 4:
                 stat = key
@@ -671,10 +668,10 @@ def writephases(arrivals, fformat, filename, parameter=None, eventinfo=None):
         fid = open("%s" % filename, 'w')
         # write header
         fid.write('%s, event %s \n' % (parameter.get('database'), parameter.get('eventID')))
-        arrivals = chooseArrivals(arrivals)
+        arrivals = chooseArrivals(arrivals)  # MP MP what is chooseArrivals? It is not defined anywhere
         for key in arrivals:
             # P onsets
-            if arrivals[key].has_key('P') and arrivals[key]['P']['mpp'] is not None:
+            if 'P' in  arrivals[key] and arrivals[key]['P']['mpp'] is not None:
                 if arrivals[key]['P']['weight'] < 4:
                     Ponset = arrivals[key]['P']['mpp']
                     pyear = Ponset.year
@@ -703,7 +700,7 @@ def writephases(arrivals, fformat, filename, parameter=None, eventinfo=None):
                     fid.write('%-5s P1       %4.0f %02d %02d %02d %02d %05.02f   %5.3f -999.   0.00 -999.  0.00\n'
                               % (key, pyear, pmonth, pday, phh, pmm, Pss, pstd))
             # S onsets
-            if arrivals[key].has_key('S') and arrivals[key]['S']['mpp'] is not None:
+            if 'S' in arrivals[key] and arrivals[key]['S']['mpp'] is not None:
                 if arrivals[key]['S']['weight'] < 4:
                     Sonset = arrivals[key]['S']['mpp']
                     syear = Sonset.year
@@ -769,10 +766,10 @@ def writephases(arrivals, fformat, filename, parameter=None, eventinfo=None):
             arrivals = picksdict_from_picks(evt)
         # check for automatic and manual picks
         # prefer manual picks
-        usedarrivals = chooseArrival(arrivals) 
+        usedarrivals = chooseArrivals(arrivals)
         for key in usedarrivals:
             # P onsets
-            if usedarrivals[key].has_key('P'):
+            if 'P' in usedarrivals[key]:
                 if usedarrivals[key]['P']['weight'] < 4:
                     n += 1
                     stat = key
@@ -786,7 +783,7 @@ def writephases(arrivals, fformat, filename, parameter=None, eventinfo=None):
                     else:
                         fid.write('%-4sP%d%6.2f\n' % (stat, Pweight, Prt))
                         # S onsets
-            if usedarrivals[key].has_key('S'):
+            if 'S' in usedarrivals[key]:
                 if usedarrivals[key]['S']['weight'] < 4:
                     n += 1
                     stat = key
@@ -815,9 +812,9 @@ def writephases(arrivals, fformat, filename, parameter=None, eventinfo=None):
             event = eventinfo['pylot_id']
             hddID = event.split('.')[0][1:5]
         except:
-            print ("Error 1111111!")
+            print("Error 1111111!")
             hddID = "00000"
-        # write header
+            # write header
         fid.write('# %d  %d %d %d %d %5.2f %7.4f +%6.4f %7.4f %4.2f 0.1 0.5 %4.2f      %s\n' % (
             stime.year, stime.month, stime.day, stime.hour, stime.minute, stime.second,
             eventsource['latitude'], eventsource['longitude'], eventsource['depth'] / 1000,
@@ -830,15 +827,15 @@ def writephases(arrivals, fformat, filename, parameter=None, eventinfo=None):
             arrivals = picksdict_from_picks(evt)
         # check for automatic and manual picks
         # prefer manual picks
-        usedarrivals = chooseArrival(arrivals)
+        usedarrivals = chooseArrivals(arrivals)
         for key in usedarrivals:
-            if usedarrivals[key].has_key('P'):
+            if 'P' in usedarrivals[key]:
                 # P onsets
                 if usedarrivals[key]['P']['weight'] < 4:
                     Ponset = usedarrivals[key]['P']['mpp']
                     Prt = Ponset - stime  # onset time relative to source time
                     fid.write('%s    %6.3f  1  P\n' % (key, Prt))
-            if usedarrivals[key].has_key('S'):
+            if 'S' in usedarrivals[key]:
                 # S onsets
                 if usedarrivals[key]['S']['weight'] < 4:
                     Sonset = usedarrivals[key]['S']['mpp']
@@ -881,9 +878,9 @@ def writephases(arrivals, fformat, filename, parameter=None, eventinfo=None):
             arrivals = picksdict_from_picks(evt)
         # check for automatic and manual picks
         # prefer manual picks
-        usedarrivals = chooseArrival(arrivals)
+        usedarrivals = chooseArrivals(arrivals)
         for key in usedarrivals:
-            if usedarrivals[key].has_key('P'):
+            if 'P' in usedarrivals[key]:
                 if usedarrivals[key]['P']['weight'] < 4 and usedarrivals[key]['P']['fm'] is not None:
                     stat = key
                     for i in range(len(picks)):
@@ -962,10 +959,10 @@ def writephases(arrivals, fformat, filename, parameter=None, eventinfo=None):
                 erh, erz, eventinfo.magnitudes[0]['mag'],
                 hashID))
         # Prefer Manual Picks over automatic ones if possible
-        arrivals = chooseArrivals(arrivals)
+        arrivals = chooseArrivals(arrivals)  # MP MP what is chooseArrivals? It is not defined anywhere
         # write phase lines
         for key in arrivals:
-            if arrivals[key].has_key('P'):
+            if 'P' in arrivals[key]:
                 if arrivals[key]['P']['weight'] < 4 and arrivals[key]['P']['fm'] is not None:
                     stat = key
                     ccode = arrivals[key]['P']['channel']
@@ -1009,7 +1006,8 @@ def writephases(arrivals, fformat, filename, parameter=None, eventinfo=None):
         fid1.close()
         fid2.close()
 
-def chooseArrival(arrivals):
+
+def chooseArrivals(arrivals):
     """
     takes arrivals and returns the manual picks if manual and automatic ones are there
     returns automatic picks if only automatic picks are there
@@ -1017,7 +1015,6 @@ def chooseArrival(arrivals):
     :return: arrivals but with the manual picks prefered if possible
     """
     # If len of arrivals is greater than 2 it comes from autopicking so only autopicks are available
-    print("=== CHOOSE ===")
     if len(arrivals) > 2:
         return arrivals
     if arrivals['auto'] and arrivals['manual']:
@@ -1058,37 +1055,63 @@ def merge_picks(event, picks):
     return event
 
 
-def getQualitiesfromxml(xmlnames, ErrorsP, ErrorsS, plotflag=1):
+def getQualitiesfromxml(path, errorsP, errorsS, plotflag=1, figure=None, verbosity=0):
     """
     Script to get onset uncertainties from Quakeml.xml files created by PyLoT.
     Uncertainties are tranformed into quality classes and visualized via histogram if desired.
     Ludger Küperkoch, BESTEC GmbH, 07/2017
-    :param xmlnames: list of xml obspy event files containing picks
+    :param path: path containing xml files
-    :type xmlnames: list
+    :type path: str
-    :param ErrorsP: time errors of P waves for the four discrete quality classes
+    :param errorsP: time errors of P waves for the four discrete quality classes
-    :type ErrorsP:
+    :type errorsP:
-    :param ErrorsS: time errors of S waves for the four discrete quality classes
+    :param errorsS: time errors of S waves for the four discrete quality classes
-    :type ErrorsS:
+    :type errorsS:
     :param plotflag:
     :type plotflag:
     :return:
     :rtype:
     """
 
-    from pylot.core.pick.utils import get_quality_class
+    def calc_perc(uncertainties, ntotal):
-    from pylot.core.util.utils import loopIdentifyPhase, identifyPhase
+        ''' simple function that calculates percentage of number of uncertainties (list length)'''
+        if len(uncertainties) == 0:
+            return 0
+        else:
+            return 100. / ntotal * len(uncertainties)
+
+    def calc_weight_perc(psweights, weight_ids):
+        ''' calculate percentages of different weights (pick classes!?) of total number of uncertainties of a phase'''
+        # count total number of list items for this phase
+        numWeights = np.sum([len(weight) for weight in psweights.values()])
+
+        # iterate over all available weights to return a list with percentages for plotting
+        plot_list = []
+        for weight_id in weight_ids:
+            plot_list.append(calc_perc(psweights[weight_id], numWeights))
+
+        return plot_list, numWeights
+
+    # get all xmlfiles in path (maybe this should be changed to one xml file for this function, selectable via GUI?)
+    xmlnames = glob.glob(os.path.join(path, '*.xml'))
+    if len(xmlnames) == 0:
+        print(f'No files found in path {path}.')
+        return False
+
+    # first define possible phases here
+    phases = ['P', 'S']
+
+    # define possible weights (0-4)
+    weight_ids = list(range(5))
+
+    # put both error lists in a dictionary with P/S key so that amount of code can be halfed by simply using P/S as key
+    errors = dict(P=errorsP, S=errorsS)
+
+    # create dictionaries for each phase (P/S) with a dictionary of empty list for each weight defined in weights
+    # tuple above
+    weights = {}
+    for phase in phases:
+        weights[phase] = {weight_id: [] for weight_id in weight_ids}
 
-    # read all onset weights
-    Pw0 = []
-    Pw1 = []
-    Pw2 = []
-    Pw3 = []
-    Pw4 = []
-    Sw0 = []
-    Sw1 = []
-    Sw2 = []
-    Sw3 = []
-    Sw4 = []
     for names in xmlnames:
         print("Getting onset weights from {}".format(names))
         cat = read_events(names)
@@ -1096,117 +1119,60 @@ def getQualitiesfromxml(xmlnames, ErrorsP, ErrorsS, plotflag=1):
         arrivals = cat.events[0].picks
         arrivals_copy = cat_copy.events[0].picks
         # Prefere manual picks if qualities are sufficient!
-        for Pick in arrivals:
+        for pick in arrivals:
-            if Pick.method_id.id.split('/')[1] == 'manual':
+            if pick.method_id.id.split('/')[1] == 'manual':
-                mstation = Pick.waveform_id.station_code
+                mstation = pick.waveform_id.station_code
                 mstation_ext = mstation + '_'
                 for mpick in arrivals_copy:
-                    phase = identifyPhase(loopIdentifyPhase(Pick.phase_hint))
+                    phase = identifyPhase(loopIdentifyPhase(pick.phase_hint)) # MP MP catch if this fails?
-                    if phase == 'P':
+                    if ((mpick.waveform_id.station_code == mstation) or
-                        if ((mpick.waveform_id.station_code == mstation) or
+                        (mpick.waveform_id.station_code == mstation_ext)) and \
-                            (mpick.waveform_id.station_code == mstation_ext)) and \
+                            (mpick.method_id.id.split('/')[1] == 'auto') and \
-                                (mpick.method_id.split('/')[1] == 'auto') and \
+                            (mpick.time_errors['uncertainty'] <= errors[phase][3]):
-                                (mpick.time_errors['uncertainty'] <= ErrorsP[3]):
+                        del mpick
-                            del mpick
+                        break
-                            break
-                    elif phase == 'S':
-                        if ((mpick.waveform_id.station_code == mstation) or
-                            (mpick.waveform_id.station_code == mstation_ext)) and \
-                                (mpick.method_id.split('/')[1] == 'auto') and \
-                                (mpick.time_errors['uncertainty'] <= ErrorsS[3]):
-                            del mpick
-                            break
         lendiff = len(arrivals) - len(arrivals_copy)
         if lendiff != 0:
             print("Found manual as well as automatic picks, prefered the {} manual ones!".format(lendiff))
 
-        for Pick in arrivals_copy:
+        for pick in arrivals_copy:
-            phase = identifyPhase(loopIdentifyPhase(Pick.phase_hint))
+            phase = identifyPhase(loopIdentifyPhase(pick.phase_hint))
-            if phase == 'P':
+            uncertainty = pick.time_errors.uncertainty
-                Pqual = get_quality_class(Pick.time_errors.uncertainty, ErrorsP)
+            if not uncertainty:
-                if Pqual == 0:
+                if verbosity > 0:
-                    Pw0.append(Pick.time_errors.uncertainty)
+                    print('No uncertainty, pick {} invalid!'.format(pick.method_id.id))
-                elif Pqual == 1:
+                continue
-                    Pw1.append(Pick.time_errors.uncertainty)
+            # check P/S phase
-                elif Pqual == 2:
+            if phase not in phases:
-                    Pw2.append(Pick.time_errors.uncertainty)
-                elif Pqual == 3:
-                    Pw3.append(Pick.time_errors.uncertainty)
-                elif Pqual == 4:
-                    Pw4.append(Pick.time_errors.uncertainty)
-            elif phase == 'S':
-                Squal = get_quality_class(Pick.time_errors.uncertainty, ErrorsS)
-                if Squal == 0:
-                    Sw0.append(Pick.time_errors.uncertainty)
-                elif Squal == 1:
-                    Sw1.append(Pick.time_errors.uncertainty)
-                elif Squal == 2:
-                    Sw2.append(Pick.time_errors.uncertainty)
-                elif Squal == 3:
-                    Sw3.append(Pick.time_errors.uncertainty)
-                elif Squal == 4:
-                    Sw4.append(Pick.time_errors.uncertainty)
-            else:
                 print("Phase hint not defined for picking!")
-                pass
+                continue
+
+            qual = get_quality_class(uncertainty, errors[phase])
+            weights[phase][qual].append(uncertainty)
 
     if plotflag == 0:
-        Punc = [Pw0, Pw1, Pw2, Pw3, Pw4]
+        p_unc = [weights['P'][weight_id] for weight_id in weight_ids]
-        Sunc = [Sw0, Sw1, Sw2, Sw3, Sw4]
+        s_unc = [weights['S'][weight_id] for weight_id in weight_ids]
-        return Punc, Sunc
+        return p_unc, s_unc
     else:
+        if not figure:
+            fig = plt.figure()
+        ax = fig.add_subplot(111)
         # get percentage of weights
-        numPweights = np.sum([len(Pw0), len(Pw1), len(Pw2), len(Pw3), len(Pw4)])
+        listP, numPweights = calc_weight_perc(weights['P'], weight_ids)
-        numSweights = np.sum([len(Sw0), len(Sw1), len(Sw2), len(Sw3), len(Sw4)])
+        listS, numSweights = calc_weight_perc(weights['S'], weight_ids)
-        if len(Pw0) > 0:
-            P0perc = 100 / numPweights * len(Pw0)
-        else:
-            P0perc = 0
-        if len(Pw1) > 0:
-            P1perc = 100 / numPweights * len(Pw1)
-        else:
-            P1perc = 0
-        if len(Pw2) > 0:
-            P2perc = 100 / numPweights * len(Pw2)
-        else:
-            P2perc = 0
-        if len(Pw3) > 0:
-            P3perc = 100 / numPweights * len(Pw3)
-        else:
-            P3perc = 0
-        if len(Pw4) > 0:
-            P4perc = 100 / numPweights * len(Pw4)
-        else:
-            P4perc = 0
-        if len(Sw0) > 0:
-            S0perc = 100 / numSweights * len(Sw0)
-        else:
-            S0perc = 0
-        if len(Sw1) > 0:
-            S1perc = 100 / numSweights * len(Sw1)
-        else:
-            S1perc = 0
-        if len(Sw2) > 0:
-            S2perc = 100 / numSweights * len(Sw2)
-        else:
-            S2perc = 0
-        if len(Sw3) > 0:
-            S3perc = 100 / numSweights * len(Sw3)
-        else:
-            S3perc = 0
-        if len(Sw4) > 0:
-            S4perc = 100 / numSweights * len(Sw4)
-        else:
-            S4perc = 0
 
-        weights = ('0', '1', '2', '3', '4')
+        y_pos = np.arange(len(weight_ids))
-        y_pos = np.arange(len(weights))
         width = 0.34
-        plt.bar(y_pos - width, [P0perc, P1perc, P2perc, P3perc, P4perc], width, color='black')
+        ax.bar(y_pos - width, listP, width, color='black')
-        plt.bar(y_pos, [S0perc, S1perc, S2perc, S3perc, S4perc], width, color='red')
+        ax.bar(y_pos, listS, width, color='red')
-        plt.ylabel('%')
+        ax.set_ylabel('%')
-        plt.xticks(y_pos, weights)
+        ax.set_xticks(y_pos, weight_ids)
-        plt.xlim([-0.5, 4.5])
+        ax.set_xlim([-0.5, 4.5])
-        plt.xlabel('Qualities')
+        ax.set_xlabel('Qualities')
-        plt.title('{0} P-Qualities, {1} S-Qualities'.format(numPweights, numSweights))
+        ax.set_title('{0} P-Qualities, {1} S-Qualities'.format(numPweights, numSweights))
-        plt.show()
+
+        if not figure:
+            fig.show()
+
+        return listP, listS

pylot/core/loc/nll.py

@@ -4,11 +4,12 @@
 import glob
 import os
 import subprocess
+
 from obspy import read_events
 
 from pylot.core.io.phases import writephases
-from pylot.core.util.utils import getPatternLine, runProgram
 from pylot.core.util.gui import which
+from pylot.core.util.utils import getPatternLine, runProgram
 from pylot.core.util.version import get_git_version as _getVersionString
 
 __version__ = _getVersionString()
@@ -81,8 +82,8 @@ def locate(fnin, parameter=None):
     :param fnin: external program name
     :return: None
     """
-    exe_path = which('NLLoc', parameter)
+    exe_path = os.path.join(parameter['nllocbin'], 'NLLoc')
-    if exe_path is None:
+    if not os.path.isfile(exe_path):
         raise NLLocError('NonLinLoc executable not found; check your '
                          'environment variables')
 

pylot/core/pick/autopick.py

@@ -9,21 +9,21 @@ function conglomerate utils.
 :author: MAGS2 EP3 working group / Ludger Kueperkoch
 """
 import copy
+import traceback
+
 import matplotlib.pyplot as plt
 import numpy as np
-import traceback
+from obspy import Trace
 from obspy.taup import TauPyModel
 
 from pylot.core.pick.charfuns import CharacteristicFunction
 from pylot.core.pick.charfuns import HOScf, AICcf, ARZcf, ARHcf, AR3Ccf
 from pylot.core.pick.picker import AICPicker, PragPicker
 from pylot.core.pick.utils import checksignallength, checkZ4S, earllatepicker, \
-    getSNR, fmpicker, checkPonsets, wadaticheck, get_pickparams, get_quality_class
+    getSNR, fmpicker, checkPonsets, wadaticheck, get_quality_class
-from pylot.core.util.utils import getPatternLine, gen_Pool,\
+from pylot.core.util.utils import getPatternLine, gen_Pool, \
-    get_Bool, identifyPhaseID, get_None, correct_iplot
+    get_bool, identifyPhaseID, get_None, correct_iplot
 
-from obspy.taup import TauPyModel
-from obspy import Trace
 
 def autopickevent(data, param, iplot=0, fig_dict=None, fig_dict_wadatijack=None, ncores=0, metadata=None, origin=None):
     """
@@ -182,25 +182,25 @@ class PickingResults(dict):
         # TODO What are those?
         self.w0 = None
         self.fc = None
-        self.Ao = None          # Wood-Anderson peak-to-peak amplitude
+        self.Ao = None  # Wood-Anderson peak-to-peak amplitude
 
         # Station information
         self.network = None
         self.channel = None
 
         # pick information
-        self.picker = 'auto'    # type of pick
+        self.picker = 'auto'  # type of pick
         self.marked = []
 
         # pick results
-        self.epp = None         # earliest possible pick
+        self.epp = None  # earliest possible pick
-        self.mpp = None         # most likely onset
+        self.mpp = None  # most likely onset
-        self.lpp = None         # latest possible pick
+        self.lpp = None  # latest possible pick
-        self.fm = 'N'           # first motion polarity, can be set to 'U' (Up) or 'D' (Down)
+        self.fm = 'N'  # first motion polarity, can be set to 'U' (Up) or 'D' (Down)
-        self.snr = None         # signal-to-noise ratio of onset
+        self.snr = None  # signal-to-noise ratio of onset
-        self.snrdb = None       # signal-to-noise ratio of onset [dB]
+        self.snrdb = None  # signal-to-noise ratio of onset [dB]
-        self.spe = None         # symmetrized picking error
+        self.spe = None  # symmetrized picking error
-        self.weight = 4         # weight of onset
+        self.weight = 4  # weight of onset
 
     # to correctly provide dot access to dictionary attributes, all attribute access of the class is forwarded to the
     # dictionary
@@ -335,9 +335,10 @@ class AutopickStation(object):
         """
         waveform_data = {}
         for key in self.channelorder:
-            waveform_data[key] = self.wfstream.select(component=key) # try ZNE first
+            waveform_data[key] = self.wfstream.select(component=key)  # try ZNE first
             if len(waveform_data[key]) == 0:
-                waveform_data[key] = self.wfstream.select(component=str(self.channelorder[key])) # use 123 as second option
+                waveform_data[key] = self.wfstream.select(
+                    component=str(self.channelorder[key]))  # use 123 as second option
         return waveform_data['Z'], waveform_data['N'], waveform_data['E']
 
     def get_traces_from_streams(self):
@@ -428,7 +429,7 @@ class AutopickStation(object):
             if station_coords is None:
                 exit_taupy()
                 raise AttributeError('Warning: Could not find station in metadata')
-            # TODO raise when metadata.get_coordinates returns None
+            # TODO: raise when metadata.get_coordinates returns None
             source_origin = origin[0]
             model = TauPyModel(taup_model)
             taup_phases = self.pickparams['taup_phases']
@@ -470,11 +471,13 @@ class AutopickStation(object):
             """If taupy failed to calculate theoretical starttimes, picking continues.
             For this a clean exit is required, since the P starttime is no longer relative to the theoretic onset but
             to the vertical trace starttime, eg. it can't be < 0."""
+            # TODO here the pickparams is modified, instead of a copy
             if self.pickparams["pstart"] < 0:
-                # TODO here the pickparams is modified, instead of a copy
                 self.pickparams["pstart"] = 0
+            if self.pickparams["sstart"] < 0:
+                self.pickparams["sstart"] = 0
 
-        if self.pickparams["use_taup"] is False:
+        if get_bool(self.pickparams["use_taup"]) is False:
             # correct user mistake where a relative cuttime is selected (pstart < 0) but use of taupy is disabled/ has
             # not the required parameters
             exit_taupy()
@@ -498,6 +501,20 @@ class AutopickStation(object):
         self.pickparams["pstart"] = max(self.pickparams["pstart"], 0)
         self.pickparams["pstop"] = min(self.pickparams["pstop"], len(self.ztrace) * self.ztrace.stats.delta)
 
+        if self.horizontal_traces_exist():
+            # for the two horizontal components take earliest and latest time to make sure that the s onset is not clipped
+            # if start and endtime of horizontal traces differ, the s windowsize will automatically increase
+            trace_s_start = min([self.etrace.stats.starttime, self.ntrace.stats.starttime])
+            # modifiy sstart and sstop relative to estimated first S arrival (relative to station time axis)
+            self.pickparams["sstart"] += (self.origin[0].time + estFirstS) - trace_s_start
+            self.pickparams["sstop"] += (self.origin[0].time + estFirstS) - trace_s_start
+            print('autopick: CF calculation times respectively:'
+                  ' sstart: {} s, sstop: {} s'.format(self.pickparams["sstart"], self.pickparams["sstop"]))
+            # make sure pstart and pstop are inside the starttime/endtime of horizontal traces
+            self.pickparams["sstart"] = max(self.pickparams["sstart"], 0)
+            self.pickparams["sstop"] = min(self.pickparams["sstop"], len(self.ntrace) * self.ntrace.stats.delta,
+                                           len(self.etrace) * self.etrace.stats.delta)
+
     def autopickstation(self):
         """
         Main function of autopickstation, which calculates P and S picks and returns them in a dictionary.
@@ -507,6 +524,17 @@ class AutopickStation(object):
         station's value is the station name on which the picks were calculated.
         :rtype: dict
         """
+
+        if get_bool(self.pickparams['use_taup']) is True and self.origin is not None:
+            try:
+                # modify pstart, pstop, sstart, sstop to be around theoretical onset if taupy should be used,
+                # else do nothing
+                self.modify_starttimes_taupy()
+            except AttributeError as ae:
+                print(ae)
+            except MissingTraceException as mte:
+                print(mte)
+
         try:
             self.pick_p_phase()
         except MissingTraceException as mte:
@@ -514,17 +542,19 @@ class AutopickStation(object):
         except PickingFailedException as pfe:
             print(pfe)
 
-        if self.horizontal_traces_exist() and self.p_results.weight is not None and self.p_results.weight < 4:
+        if self.horizontal_traces_exist():
-            try:
+            if (self.p_results.weight is not None and self.p_results.weight < 4) or \
-                self.pick_s_phase()
+                    get_bool(self.pickparams.get('use_taup')):
-            except MissingTraceException as mte:
+                try:
-                print(mte)
+                    self.pick_s_phase()
-            except PickingFailedException as pfe:
+                except MissingTraceException as mte:
-                print(pfe)
+                    print(mte)
+                except PickingFailedException as pfe:
+                    print(pfe)
 
         self.plot_pick_results()
         self.finish_picking()
-        return [{'P': self.p_results, 'S':self.s_results}, self.ztrace.stats.station]
+        return [{'P': self.p_results, 'S': self.s_results}, self.ztrace.stats.station]
 
     def finish_picking(self):
 
@@ -573,7 +603,7 @@ class AutopickStation(object):
 
         self.s_results.channel = self.etrace.stats.channel
         self.s_results.network = self.etrace.stats.network
-        self.s_results.fm = None    # override default value 'N'
+        self.s_results.fm = None  # override default value 'N'
 
     def plot_pick_results(self):
         if self.iplot > 0:
@@ -588,12 +618,14 @@ class AutopickStation(object):
                 plt_flag = 0
             fig._tight = True
             ax1 = fig.add_subplot(311)
-            tdata = np.linspace(start=0, stop=self.ztrace.stats.endtime-self.ztrace.stats.starttime, num=self.ztrace.stats.npts)
+            tdata = np.linspace(start=0, stop=self.ztrace.stats.endtime - self.ztrace.stats.starttime,
+                                num=self.ztrace.stats.npts)
             # plot tapered trace filtered with bpz2 filter settings
-            ax1.plot(tdata, self.tr_filt_z_bpz2.data/max(self.tr_filt_z_bpz2.data), color=linecolor, linewidth=0.7, label='Data')
+            ax1.plot(tdata, self.tr_filt_z_bpz2.data / max(self.tr_filt_z_bpz2.data), color=linecolor, linewidth=0.7,
+                     label='Data')
             if self.p_results.weight < 4:
                 # plot CF of initial onset (HOScf or ARZcf)
-                ax1.plot(self.cf1.getTimeArray(), self.cf1.getCF()/max(self.cf1.getCF()), 'b', label='CF1')
+                ax1.plot(self.cf1.getTimeArray(), self.cf1.getCF() / max(self.cf1.getCF()), 'b', label='CF1')
             if self.p_data.p_aic_plot_flag == 1:
                 aicpick = self.p_data.aicpick
                 refPpick = self.p_data.refPpick
@@ -631,35 +663,41 @@ class AutopickStation(object):
             if self.horizontal_traces_exist() and self.s_data.Sflag == 1:
                 # plot E trace
                 ax2 = fig.add_subplot(3, 1, 2, sharex=ax1)
-                th1data = np.linspace(0, self.etrace.stats.endtime-self.etrace.stats.starttime, self.etrace.stats.npts)
+                th1data = np.linspace(0, self.etrace.stats.endtime - self.etrace.stats.starttime,
+                                      self.etrace.stats.npts)
                 # plot filtered and tapered waveform
-                ax2.plot(th1data, self.etrace.data / max(self.etrace.data), color=linecolor, linewidth=0.7, label='Data')
+                ax2.plot(th1data, self.etrace.data / max(self.etrace.data), color=linecolor, linewidth=0.7,
+                         label='Data')
                 if self.p_results.weight < 4:
                     # plot initial CF (ARHcf or AR3Ccf)
-                    ax2.plot(self.arhcf1.getTimeArray(), self.arhcf1.getCF() / max(self.arhcf1.getCF()), 'b', label='CF1')
+                    ax2.plot(self.arhcf1.getTimeArray(), self.arhcf1.getCF() / max(self.arhcf1.getCF()), 'b',
+                             label='CF1')
                     if self.s_data.aicSflag == 1 and self.s_results.weight <= 4:
                         aicarhpick = self.aicarhpick
                         refSpick = self.refSpick
                         # plot second cf, used for determing precise onset (ARHcf or AR3Ccf)
-                        ax2.plot(self.arhcf2.getTimeArray(), self.arhcf2.getCF() / max(self.arhcf2.getCF()), 'm', label='CF2')
+                        ax2.plot(self.arhcf2.getTimeArray(), self.arhcf2.getCF() / max(self.arhcf2.getCF()), 'm',
+                                 label='CF2')
                         # plot preliminary onset time, calculated from CF1
                         ax2.plot([aicarhpick.getpick(), aicarhpick.getpick()], [-1, 1], 'g', label='Initial S Onset')
                         ax2.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [1, 1], 'g')
                         ax2.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [-1, -1], 'g')
                         # plot precise onset time, calculated from CF2
-                        ax2.plot([refSpick.getpick(), refSpick.getpick()], [-1.3, 1.3], 'g', linewidth=2, label='Final S Pick')
+                        ax2.plot([refSpick.getpick(), refSpick.getpick()], [-1.3, 1.3], 'g', linewidth=2,
+                                 label='Final S Pick')
                         ax2.plot([refSpick.getpick() - 0.5, refSpick.getpick() + 0.5], [1.3, 1.3], 'g', linewidth=2)
                         ax2.plot([refSpick.getpick() - 0.5, refSpick.getpick() + 0.5], [-1.3, -1.3], 'g', linewidth=2)
                         ax2.plot([self.s_results.lpp, self.s_results.lpp], [-1.1, 1.1], 'g--', label='lpp')
                         ax2.plot([self.s_results.epp, self.s_results.epp], [-1.1, 1.1], 'g--', label='epp')
                         title = '{channel}, S weight={sweight}, SNR={snr:7.2}, SNR[dB]={snrdb:7.2}'
-                        ax2.set_title(title.format(channel=self.etrace.stats.channel,
+                        ax2.set_title(title.format(channel=str(self.etrace.stats.channel),
-                                                   sweight=self.s_results.weight,
+                                                   sweight=str(self.s_results.weight),
-                                                   snr=self.s_results.snr,
+                                                   snr=str(self.s_results.snr),
-                                                   snrdb=self.s_results.snrdb))
+                                                   snrdb=str(self.s_results.snrdb)))
                     else:
                         title = '{channel}, S weight={sweight}, SNR=None, SNR[dB]=None'
-                        ax2.set_title(title.format(channel=self.etrace.stats.channel, sweight=self.s_results.weight))
+                        ax2.set_title(title.format(channel=str(self.etrace.stats.channel),
+                                                   sweight=str(self.s_results.weight)))
                 ax2.legend(loc=1)
                 ax2.set_yticks([])
                 ax2.set_ylim([-1.5, 1.5])
@@ -667,15 +705,19 @@ class AutopickStation(object):
 
                 # plot N trace
                 ax3 = fig.add_subplot(3, 1, 3, sharex=ax1)
-                th2data= np.linspace(0, self.ntrace.stats.endtime-self.ntrace.stats.starttime, self.ntrace.stats.npts)
+                th2data = np.linspace(0, self.ntrace.stats.endtime - self.ntrace.stats.starttime,
+                                      self.ntrace.stats.npts)
                 # plot trace
-                ax3.plot(th2data, self.ntrace.data / max(self.ntrace.data), color=linecolor, linewidth=0.7, label='Data')
+                ax3.plot(th2data, self.ntrace.data / max(self.ntrace.data), color=linecolor, linewidth=0.7,
+                         label='Data')
                 if self.p_results.weight < 4:
-                    p22, = ax3.plot(self.arhcf1.getTimeArray(), self.arhcf1.getCF() / max(self.arhcf1.getCF()), 'b', label='CF1')
+                    p22, = ax3.plot(self.arhcf1.getTimeArray(), self.arhcf1.getCF() / max(self.arhcf1.getCF()), 'b',
+                                    label='CF1')
                     if self.s_data.aicSflag == 1:
                         aicarhpick = self.aicarhpick
                         refSpick = self.refSpick
-                        ax3.plot(self.arhcf2.getTimeArray(), self.arhcf2.getCF() / max(self.arhcf2.getCF()), 'm', label='CF2')
+                        ax3.plot(self.arhcf2.getTimeArray(), self.arhcf2.getCF() / max(self.arhcf2.getCF()), 'm',
+                                 label='CF2')
                         ax3.plot([aicarhpick.getpick(), aicarhpick.getpick()], [-1, 1], 'g', label='Initial S Onset')
                         ax3.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [1, 1], 'g')
                         ax3.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [-1, -1], 'g')
@@ -716,7 +758,8 @@ class AutopickStation(object):
         if aicpick.getpick() is None:
             msg = "Bad initial (AIC) P-pick, skipping this onset!\nAIC-SNR={0}, AIC-Slope={1}counts/s\n " \
                   "(min. AIC-SNR={2}, min. AIC-Slope={3}counts/s)"
-            msg = msg.format(aicpick.getSNR(), aicpick.getSlope(), self.pickparams["minAICPSNR"], self.pickparams["minAICPslope"])
+            msg = msg.format(aicpick.getSNR(), aicpick.getSlope(), self.pickparams["minAICPSNR"],
+                             self.pickparams["minAICPslope"])
             self.vprint(msg)
             return 0
         # Quality check initial pick with minimum signal length
@@ -726,14 +769,16 @@ class AutopickStation(object):
         if len(self.nstream) == 0 or len(self.estream) == 0:
             msg = 'One or more horizontal component(s) missing!\n' \
                   'Signal length only checked on vertical component!\n' \
-                  'Decreasing minsiglengh from {0} to {1}'\
+                  'Decreasing minsiglengh from {0} to {1}' \
-                  .format(minsiglength, minsiglength / 2)
+                .format(minsiglength, minsiglength / 2)
             self.vprint(msg)
             minsiglength = minsiglength / 2
         else:
             # filter, taper other traces as well since signal length is compared on all traces
-            trH1_filt, _ = self.prepare_wfstream(self.estream, freqmin=self.pickparams["bph1"][0], freqmax=self.pickparams["bph1"][1])
+            trH1_filt, _ = self.prepare_wfstream(self.estream, freqmin=self.pickparams["bph1"][0],
-            trH2_filt, _ = self.prepare_wfstream(self.nstream, freqmin=self.pickparams["bph1"][0], freqmax=self.pickparams["bph1"][1])
+                                                 freqmax=self.pickparams["bph1"][1])
+            trH2_filt, _ = self.prepare_wfstream(self.nstream, freqmin=self.pickparams["bph1"][0],
+                                                 freqmax=self.pickparams["bph1"][1])
             zne += trH1_filt
             zne += trH2_filt
             minsiglength = minsiglength
@@ -780,15 +825,6 @@ class AutopickStation(object):
         # save filtered trace in instance for later plotting
         self.tr_filt_z_bpz2 = tr_filt
 
-        if get_Bool(self.pickparams['use_taup']) is True and self.origin is not None:
-            try:
-                # modify pstart, pstop to be around theoretical onset if taupy should be used, else does nothing
-                self.modify_starttimes_taupy()
-            except AttributeError as ae:
-                print(ae)
-            except MissingTraceException as mte:
-                print(mte)
-
         Lc = self.pickparams['pstop'] - self.pickparams['pstart']
 
         Lwf = self.ztrace.stats.endtime - self.ztrace.stats.starttime
@@ -819,15 +855,18 @@ class AutopickStation(object):
         # get preliminary onset time from AIC-CF
         self.set_current_figure('aicFig')
         aicpick = AICPicker(aiccf, self.pickparams["tsnrz"], self.pickparams["pickwinP"], self.iplot,
-                            Tsmooth=self.pickparams["aictsmooth"], fig=self.current_figure, linecolor=self.current_linecolor)
+                            Tsmooth=self.pickparams["aictsmooth"], fig=self.current_figure,
+                            linecolor=self.current_linecolor)
         # save aicpick for plotting later
         self.p_data.aicpick = aicpick
         # add pstart and pstop to aic plot
         if self.current_figure:
             # TODO remove plotting from picking, make own plot function
             for ax in self.current_figure.axes:
-                ax.vlines(self.pickparams["pstart"], ax.get_ylim()[0], ax.get_ylim()[1], color='c', linestyles='dashed', label='P start')
+                ax.vlines(self.pickparams["pstart"], ax.get_ylim()[0], ax.get_ylim()[1], color='c', linestyles='dashed',
-                ax.vlines(self.pickparams["pstop"], ax.get_ylim()[0], ax.get_ylim()[1], color='c', linestyles='dashed', label='P stop')
+                          label='P start')
+                ax.vlines(self.pickparams["pstop"], ax.get_ylim()[0], ax.get_ylim()[1], color='c', linestyles='dashed',
+                          label='P stop')
                 ax.legend(loc=1)
 
         Pflag = self._pick_p_quality_control(aicpick, z_copy, tr_filt)
@@ -841,7 +880,8 @@ class AutopickStation(object):
             error_msg = 'AIC P onset slope to small: got {}, min {}'.format(slope, self.pickparams["minAICPslope"])
             raise PickingFailedException(error_msg)
         if aicpick.getSNR() < self.pickparams["minAICPSNR"]:
-            error_msg = 'AIC P onset SNR to small: got {}, min {}'.format(aicpick.getSNR(), self.pickparams["minAICPSNR"])
+            error_msg = 'AIC P onset SNR to small: got {}, min {}'.format(aicpick.getSNR(),
+                                                                          self.pickparams["minAICPSNR"])
             raise PickingFailedException(error_msg)
 
         self.p_data.p_aic_plot_flag = 1
@@ -849,7 +889,8 @@ class AutopickStation(object):
               'autopickstation: re-filtering vertical trace...'.format(aicpick.getSlope(), aicpick.getSNR())
         self.vprint(msg)
         # refilter waveform with larger bandpass
-        tr_filt, z_copy = self.prepare_wfstream(self.zstream, freqmin=self.pickparams["bpz2"][0], freqmax=self.pickparams["bpz2"][1])
+        tr_filt, z_copy = self.prepare_wfstream(self.zstream, freqmin=self.pickparams["bpz2"][0],
+                                                freqmax=self.pickparams["bpz2"][1])
         # save filtered trace in instance for later plotting
         self.tr_filt_z_bpz2 = tr_filt
         # determine new times around initial onset
@@ -861,25 +902,29 @@ class AutopickStation(object):
         else:
             self.cf2 = None
         assert isinstance(self.cf2, CharacteristicFunction), 'cf2 is not set correctly: maybe the algorithm name () is ' \
-                                                        'corrupted'.format(self.pickparams["algoP"])
+                                                             'corrupted'.format(self.pickparams["algoP"])
         self.set_current_figure('refPpick')
         # get refined onset time from CF2
-        refPpick = PragPicker(self.cf2, self.pickparams["tsnrz"], self.pickparams["pickwinP"], self.iplot, self.pickparams["ausP"],
+        refPpick = PragPicker(self.cf2, self.pickparams["tsnrz"], self.pickparams["pickwinP"], self.iplot,
-                              self.pickparams["tsmoothP"], aicpick.getpick(), self.current_figure, self.current_linecolor)
+                              self.pickparams["ausP"],
+                              self.pickparams["tsmoothP"], aicpick.getpick(), self.current_figure,
+                              self.current_linecolor)
         # save PragPicker result for plotting
         self.p_data.refPpick = refPpick
         self.p_results.mpp = refPpick.getpick()
         if self.p_results.mpp is None:
             msg = 'Bad initial (AIC) P-pick, skipping this onset!\n AIC-SNR={}, AIC-Slope={}counts/s\n' \
                   '(min. AIC-SNR={}, min. AIC-Slope={}counts/s)'
-            msg.format(aicpick.getSNR(), aicpick.getSlope(), self.pickparams["minAICPSNR"], self.pickparams["minAICPslope"])
+            msg.format(aicpick.getSNR(), aicpick.getSlope(), self.pickparams["minAICPSNR"],
+                       self.pickparams["minAICPslope"])
             self.vprint(msg)
             self.s_data.Sflag = 0
             raise PickingFailedException(msg)
         # quality assessment, get earliest/latest pick and symmetrized uncertainty
-        #todo quality assessment in own function
+        # todo quality assessment in own function
         self.set_current_figure('el_Ppick')
-        elpicker_results = earllatepicker(z_copy, self.pickparams["nfacP"], self.pickparams["tsnrz"], self.p_results.mpp,
+        elpicker_results = earllatepicker(z_copy, self.pickparams["nfacP"], self.pickparams["tsnrz"],
+                                          self.p_results.mpp,
                                           self.iplot, fig=self.current_figure, linecolor=self.current_linecolor)
         self.p_results.epp, self.p_results.lpp, self.p_results.spe = elpicker_results
         snr_results = getSNR(z_copy, self.pickparams["tsnrz"], self.p_results.mpp)
@@ -887,10 +932,11 @@ class AutopickStation(object):
 
         # weight P-onset using symmetric error
         self.p_results.weight = get_quality_class(self.p_results.spe, self.pickparams["timeerrorsP"])
-        if self.p_results.weight <= self.pickparams["minfmweight"] and self.p_results.snr >= self.pickparams["minFMSNR"]:
+        if self.p_results.weight <= self.pickparams["minfmweight"] and self.p_results.snr >= self.pickparams[
+            "minFMSNR"]:
             # if SNR is high enough, try to determine first motion of onset
             self.set_current_figure('fm_picker')
-            self.p_results.fm = fmpicker(self.zstream, z_copy, self.pickparams["fmpickwin"], self.p_results.mpp,
+            self.p_results.fm = fmpicker(self.zstream.copy(), z_copy, self.pickparams["fmpickwin"], self.p_results.mpp,
                                          self.iplot, self.current_figure, self.current_linecolor)
         msg = "autopickstation: P-weight: {}, SNR: {}, SNR[dB]: {}, Polarity: {}"
         msg = msg.format(self.p_results.weight, self.p_results.snr, self.p_results.snrdb, self.p_results.fm)
@@ -960,7 +1006,7 @@ class AutopickStation(object):
             trH1_filt, _ = self.prepare_wfstream(self.zstream, filter_freq_min, filter_freq_max)
             trH2_filt, _ = self.prepare_wfstream(self.estream, filter_freq_min, filter_freq_max)
             trH3_filt, _ = self.prepare_wfstream(self.nstream, filter_freq_min, filter_freq_max)
-            h_copy =self. hdat.copy()
+            h_copy = self.hdat.copy()
             h_copy[0].data = trH1_filt.data
             h_copy[1].data = trH2_filt.data
             h_copy[2].data = trH3_filt.data
@@ -1102,9 +1148,11 @@ class AutopickStation(object):
                   ''.format(self.s_results.weight, self.s_results.snr, self.s_results.snrdb))
 
     def pick_s_phase(self):
-
+        if get_bool(self.pickparams.get('use_taup')) is True:
-        # determine time window for calculating CF after P onset
+            cuttimesh = (self.pickparams.get('sstart'), self.pickparams.get('sstop'))
-        cuttimesh = self._calculate_cuttimes(type='S', iteration=1)
+        else:
+            # determine time window for calculating CF after P onset
+            cuttimesh = self._calculate_cuttimes(type='S', iteration=1)
 
         # calculate autoregressive CF
         self.arhcf1 = self._calculate_autoregressive_cf_s_pick(cuttimesh)
@@ -1115,7 +1163,8 @@ class AutopickStation(object):
         # get preliminary onset time from AIC cf
         self.set_current_figure('aicARHfig')
         aicarhpick = AICPicker(haiccf, self.pickparams["tsnrh"], self.pickparams["pickwinS"], self.iplot,
-                               Tsmooth=self.pickparams["aictsmoothS"], fig=self.current_figure, linecolor=self.current_linecolor)
+                               Tsmooth=self.pickparams["aictsmoothS"], fig=self.current_figure,
+                               linecolor=self.current_linecolor)
         # save pick for later plotting
         self.aicarhpick = aicarhpick
 
@@ -1126,8 +1175,10 @@ class AutopickStation(object):
 
         # get refined onset time from CF2
         self.set_current_figure('refSpick')
-        refSpick = PragPicker(arhcf2, self.pickparams["tsnrh"], self.pickparams["pickwinS"], self.iplot, self.pickparams["ausS"],
+        refSpick = PragPicker(arhcf2, self.pickparams["tsnrh"], self.pickparams["pickwinS"], self.iplot,
-                              self.pickparams["tsmoothS"], aicarhpick.getpick(), self.current_figure, self.current_linecolor)
+                              self.pickparams["ausS"],
+                              self.pickparams["tsmoothS"], aicarhpick.getpick(), self.current_figure,
+                              self.current_linecolor)
         # save refSpick for later plotitng
         self.refSpick = refSpick
         self.s_results.mpp = refSpick.getpick()
@@ -1151,7 +1202,6 @@ class AutopickStation(object):
         self.current_linecolor = plot_style['linecolor']['rgba_mpl']
 
 
-
 def autopickstation(wfstream, pickparam, verbose=False, iplot=0, fig_dict=None, metadata=None, origin=None):
     """
     Main function to calculate picks for the station.
@@ -1239,11 +1289,11 @@ def iteratepicker(wf, NLLocfile, picks, badpicks, pickparameter, fig_dict=None):
         print(
             "iteratepicker: The following picking parameters have been modified for iterative picking:")
         print(
-                "pstart: %fs => %fs" % (pstart_old, pickparameter.get('pstart')))
+            "pstart: %fs => %fs" % (pstart_old, pickparameter.get('pstart')))
         print(
-                "pstop: %fs => %fs" % (pstop_old, pickparameter.get('pstop')))
+            "pstop: %fs => %fs" % (pstop_old, pickparameter.get('pstop')))
         print(
-                "sstop: %fs => %fs" % (sstop_old, pickparameter.get('sstop')))
+            "sstop: %fs => %fs" % (sstop_old, pickparameter.get('sstop')))
         print("pickwinP: %fs => %fs" % (
             pickwinP_old, pickparameter.get('pickwinP')))
         print("Precalcwin: %fs => %fs" % (

pylot/core/pick/charfuns.py

@@ -16,7 +16,6 @@ autoregressive prediction: application ot local and regional distances, Geophys.
 
 :author: MAGS2 EP3 working group
 """
-
 import numpy as np
 from scipy import signal
 from obspy.core import Stream
@@ -159,7 +158,7 @@ class CharacteristicFunction(object):
                 zz = self.orig_data.copy()
                 z1 = zz[0].copy()
                 zz[0].data = z1.data[int(start):int(stop)]
-                if zz[0].stats.npts == 0: # cut times do not fit data length!
+                if zz[0].stats.npts == 0:  # cut times do not fit data length!
                     zz[0].data = z1.data  # take entire data
                 data = zz
                 return data
@@ -259,7 +258,7 @@ class HOScf(CharacteristicFunction):
         """
         Function to calculate skewness (statistics of order 3) or kurtosis
         (statistics of order 4), using one long moving window, as published
-        in Kueperkoch et al. (2010).
+        in Kueperkoch et al. (2010), or order 2, i.e. STA/LTA.
         :param data: data, time series (whether seismogram or CF)
         :type data: tuple
         :return: HOS cf
@@ -276,28 +275,47 @@ class HOScf(CharacteristicFunction):
         elif self.getOrder() == 4:  # this is kurtosis
             y = np.power(xnp, 4)
             y1 = np.power(xnp, 2)
+        elif self.getOrder() == 2:  # this is variance, used for STA/LTA processing
+            y = np.power(xnp, 2)
+            y1 = np.power(xnp, 2)
 
         # Initialisation
         # t2: long term moving window
         ilta = int(round(self.getTime2() / self.getIncrement()))
+        ista = int(round((self.getTime2() / 10) / self.getIncrement()))  # TODO: still hard coded!!
         lta = y[0]
         lta1 = y1[0]
+        sta = y[0]
         # moving windows
         LTA = np.zeros(len(xnp))
+        STA = np.zeros(len(xnp))
         for j in range(0, len(xnp)):
             if j < 4:
                 LTA[j] = 0
+                STA[j] = 0
+            elif j <= ista:
+                lta = (y[j] + lta * (j - 1)) / j
+                if self.getOrder() == 2:
+                    sta = (y[j] + sta * (j - 1)) / j
+            # elif j < 4:
             elif j <= ilta:
                 lta = (y[j] + lta * (j - 1)) / j
                 lta1 = (y1[j] + lta1 * (j - 1)) / j
+                if self.getOrder() == 2:
+                    sta = (y[j] - y[j - ista]) / ista + sta
             else:
                 lta = (y[j] - y[j - ilta]) / ilta + lta
                 lta1 = (y1[j] - y1[j - ilta]) / ilta + lta1
+                if self.getOrder() == 2:
+                    sta = (y[j] - y[j - ista]) / ista + sta
             # define LTA
             if self.getOrder() == 3:
                 LTA[j] = lta / np.power(lta1, 1.5)
             elif self.getOrder() == 4:
                 LTA[j] = lta / np.power(lta1, 2)
+            else:
+                LTA[j] = lta
+                STA[j] = sta
 
         # remove NaN's with first not-NaN-value,
         # so autopicker doesnt pick discontinuity at start of the trace
@@ -306,14 +324,18 @@ class HOScf(CharacteristicFunction):
             first = ind[0]
             LTA[:first] = LTA[first]
 
-        self.cf = LTA
+        if self.getOrder() > 2:
+            self.cf = LTA
+        else:  # order 2 means STA/LTA!
+            self.cf = STA / LTA
         self.xcf = x
 
 
 class ARZcf(CharacteristicFunction):
 
     def __init__(self, data, cut, t1, t2, pickparams):
-        super(ARZcf, self).__init__(data, cut, t1=t1, t2=t2, order=pickparams["Parorder"], fnoise=pickparams["addnoise"])
+        super(ARZcf, self).__init__(data, cut, t1=t1, t2=t2, order=pickparams["Parorder"],
+                                    fnoise=pickparams["addnoise"])
 
     def calcCF(self, data):
         """
@@ -448,7 +470,8 @@ class ARZcf(CharacteristicFunction):
 class ARHcf(CharacteristicFunction):
 
     def __init__(self, data, cut, t1, t2, pickparams):
-        super(ARHcf, self).__init__(data, cut, t1=t1, t2=t2, order=pickparams["Sarorder"], fnoise=pickparams["addnoise"])
+        super(ARHcf, self).__init__(data, cut, t1=t1, t2=t2, order=pickparams["Sarorder"],
+                                    fnoise=pickparams["addnoise"])
 
     def calcCF(self, data):
         """
@@ -600,7 +623,8 @@ class ARHcf(CharacteristicFunction):
 class AR3Ccf(CharacteristicFunction):
 
     def __init__(self, data, cut, t1, t2, pickparams):
-        super(AR3Ccf, self).__init__(data, cut, t1=t1, t2=t2, order=pickparams["Sarorder"], fnoise=pickparams["addnoise"])
+        super(AR3Ccf, self).__init__(data, cut, t1=t1, t2=t2, order=pickparams["Sarorder"],
+                                     fnoise=pickparams["addnoise"])
 
     def calcCF(self, data):
         """

pylot/core/pick/compare.py

@@ -2,10 +2,11 @@
 # -*- coding: utf-8 -*-
 
 import copy
-import matplotlib.pyplot as plt
-import numpy as np
 import operator
 import os
+
+import matplotlib.pyplot as plt
+import numpy as np
 from obspy.core import AttribDict
 
 from pylot.core.util.pdf import ProbabilityDensityFunction
@@ -400,6 +401,7 @@ class PDFstatistics(object):
     This object can be used to get various statistic values from probability density functions.
     Takes a path as argument.
     """
+
     # TODO: change root to datapath
 
     def __init__(self, directory):

pylot/core/pick/picker.py

@@ -19,9 +19,10 @@ calculated after Diehl & Kissling (2009).
 :author: MAGS2 EP3 working group / Ludger Kueperkoch
 """
 
+import warnings
+
 import matplotlib.pyplot as plt
 import numpy as np
-import warnings
 from scipy.signal import argrelmax, argrelmin
 
 from pylot.core.pick.charfuns import CharacteristicFunction
@@ -335,7 +336,7 @@ class AICPicker(AutoPicker):
                     self.slope = 1 / (len(dataslope) * self.Data[0].stats.delta) * (datafit[-1] - datafit[0])
                     # normalize slope to maximum of cf to make it unit independent
                     self.slope /= aicsmooth[iaicmax]
-            except ValueError as e:
+            except Exception as e:
                 print("AICPicker: Problems with data fitting! {}".format(e))
 
         else:
@@ -476,7 +477,7 @@ class PragPicker(AutoPicker):
             cfpick_r = 0
             cfpick_l = 0
             lpickwindow = int(round(self.PickWindow / self.dt))
-            #for i in range(max(np.insert(ipick, 0, 2)), min([ipick1 + lpickwindow + 1, len(self.cf) - 1])):
+            # for i in range(max(np.insert(ipick, 0, 2)), min([ipick1 + lpickwindow + 1, len(self.cf) - 1])):
             #    # local minimum
             #    if self.cf[i + 1] > self.cf[i] <= self.cf[i - 1]:
             #        if cfsmooth[i - 1] * (1 + aus1) >= cfsmooth[i]:
@@ -508,9 +509,9 @@ class PragPicker(AutoPicker):
             if flagpick_l > 0 and flagpick_r > 0 and cfpick_l <= 3 * cfpick_r:
                 self.Pick = pick_l
                 pickflag = 1
-            elif flagpick_l > 0 and flagpick_r > 0 and cfpick_l >= cfpick_r:
+            # elif flagpick_l > 0 and flagpick_r > 0 and cfpick_l >= cfpick_r:
-                self.Pick = pick_r
+            #     self.Pick = pick_r
-                pickflag = 1
+            #     pickflag = 1
             elif flagpick_l == 0 and flagpick_r > 0 and cfpick_l >= cfpick_r:
                 self.Pick = pick_l
                 pickflag = 1

pylot/core/pick/utils.py

@@ -9,12 +9,13 @@
 """
 
 import warnings
+
 import matplotlib.pyplot as plt
 import numpy as np
-from scipy.signal import argrelmax
 from obspy.core import Stream, UTCDateTime
+from scipy.signal import argrelmax
 
-from pylot.core.util.utils import get_Bool, get_None, SetChannelComponents
+from pylot.core.util.utils import get_bool, get_None, SetChannelComponents
 
 
 def earllatepicker(X, nfac, TSNR, Pick1, iplot=0, verbosity=1, fig=None, linecolor='k'):
@@ -61,8 +62,8 @@ def earllatepicker(X, nfac, TSNR, Pick1, iplot=0, verbosity=1, fig=None, linecol
     plt_flag = 0
     try:
         iplot = int(iplot)
-    except:
+    except ValueError:
-        if get_Bool(iplot):
+        if get_bool(iplot):
             iplot = 2
         else:
             iplot = 0
@@ -73,7 +74,7 @@ def earllatepicker(X, nfac, TSNR, Pick1, iplot=0, verbosity=1, fig=None, linecol
 
     x = X[0].data
     t = np.linspace(0, X[0].stats.endtime - X[0].stats.starttime,
-                  X[0].stats.npts)
+                    X[0].stats.npts)
     inoise = getnoisewin(t, Pick1, TSNR[0], TSNR[1])
     # get signal window
     isignal = getsignalwin(t, Pick1, TSNR[2])
@@ -218,7 +219,7 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=0, fig=None, linecolor='k'):
         xraw = Xraw[0].data
         xfilt = Xfilt[0].data
         t = np.linspace(0, Xraw[0].stats.endtime - Xraw[0].stats.starttime,
-                      Xraw[0].stats.npts)
+                        Xraw[0].stats.npts)
         # get pick window
         ipick = np.where((t <= min([Pick + pickwin, len(Xraw[0])])) & (t >= Pick))
         if len(ipick[0]) <= 1:
@@ -274,7 +275,7 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=0, fig=None, linecolor='k'):
             try:
                 P1 = np.polyfit(xslope1, xraw[islope1], 1)
                 datafit1 = np.polyval(P1, xslope1)
-            except ValueError as e:
+            except Exception as e:
                 print("fmpicker: Problems with data fit! {}".format(e))
                 print("Skip first motion determination!")
                 return FM
@@ -320,7 +321,7 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=0, fig=None, linecolor='k'):
             try:
                 P2 = np.polyfit(xslope2, xfilt[islope2], 1)
                 datafit2 = np.polyval(P2, xslope2)
-            except ValueError as e:
+            except Exception as e:
                 emsg = 'fmpicker: polyfit failed: {}'.format(e)
                 print(emsg)
                 return FM
@@ -536,9 +537,10 @@ def getslopewin(Tcf, Pick, tslope):
     :rtype: `numpy.ndarray`
     """
     # TODO: fill out docstring
-    slope = np.where( (Tcf <= min(Pick + tslope, Tcf[-1])) & (Tcf >= Pick) )
+    slope = np.where((Tcf <= min(Pick + tslope, Tcf[-1])) & (Tcf >= Pick))
     return slope[0]
 
+
 def getResolutionWindow(snr, extent):
     """
     Produce the half of the time resolution window width from given SNR value
@@ -814,7 +816,7 @@ def checksignallength(X, pick, minsiglength, pickparams, iplot=0, fig=None, line
     try:
         iplot = int(iplot)
     except:
-        if get_Bool(iplot):
+        if get_bool(iplot):
             iplot = 2
         else:
             iplot = 0
@@ -1128,7 +1130,7 @@ def checkZ4S(X, pick, pickparams, iplot, fig=None, linecolor='k'):
     try:
         iplot = int(iplot)
     except:
-        if get_Bool(iplot):
+        if get_bool(iplot):
             iplot = 2
         else:
             iplot = 0
@@ -1207,7 +1209,7 @@ def checkZ4S(X, pick, pickparams, iplot, fig=None, linecolor='k'):
             rms = rms_dict[key]
             trace = traces_dict[key]
             t = np.linspace(diff_dict[key], trace.stats.endtime - trace.stats.starttime + diff_dict[key],
-                          trace.stats.npts)
+                            trace.stats.npts)
             if i == 0:
                 if get_None(fig) is None:
                     fig = plt.figure()  # self.iplot) ### WHY? MP MP
@@ -1318,7 +1320,7 @@ def get_quality_class(uncertainty, weight_classes):
     :return: quality of pick (0-4)
     :rtype: int
     """
-    if not uncertainty: return max(weight_classes)
+    if not uncertainty: return len(weight_classes)
     try:
         # create generator expression containing all indices of values in weight classes that are >= than uncertainty.
         # call next on it once to receive first value
@@ -1329,6 +1331,7 @@ def get_quality_class(uncertainty, weight_classes):
         quality = len(weight_classes)
     return quality
 
+
 def set_NaNs_to(data, nan_value):
     """
     Replace all NaNs in data with nan_value
@@ -1344,6 +1347,7 @@ def set_NaNs_to(data, nan_value):
         data[nn] = nan_value
     return data
 
+
 def taper_cf(cf):
     """
     Taper cf data to get rid off of side maximas
@@ -1355,6 +1359,7 @@ def taper_cf(cf):
     tap = np.hanning(len(cf))
     return tap * cf
 
+
 def cf_positive(cf):
     """
     Shifts cf so that all values are positive
@@ -1365,6 +1370,7 @@ def cf_positive(cf):
     """
     return cf + max(abs(cf))
 
+
 def smooth_cf(cf, t_smooth, delta):
     """
     Smooth cf by taking samples over t_smooth length
@@ -1393,6 +1399,7 @@ def smooth_cf(cf, t_smooth, delta):
     cf_smooth -= offset  # remove offset from smoothed function
     return cf_smooth
 
+
 def check_counts_ms(data):
     """
     check if data is in counts or m/s
@@ -1452,9 +1459,9 @@ def calcSlope(Data, datasmooth, Tcf, Pick, TSNR):
         if imax == 0:
             print("AICPicker: Maximum for slope determination right at the beginning of the window!")
             print("Choose longer slope determination window!")
-            raise  IndexError
+            raise IndexError
         iislope = islope[0][0:imax + 1]  # cut index so it contains only the first maximum
-    dataslope = Data[0].data[iislope] # slope will only be calculated to the first maximum
+    dataslope = Data[0].data[iislope]  # slope will only be calculated to the first maximum
     # calculate slope as polynomal fit of order 1
     xslope = np.arange(0, len(dataslope))
     P = np.polyfit(xslope, dataslope, 1)
@@ -1475,8 +1482,10 @@ def get_pickparams(pickparam):
     :rtype: (dict, dict, dict, dict)
     """
     # Define names of all parameters in different groups
-    p_parameter_names = 'algoP pstart pstop use_taup taup_model tlta tsnrz hosorder bpz1 bpz2 pickwinP aictsmooth tsmoothP ausP nfacP tpred1z tdet1z Parorder addnoise Precalcwin minAICPslope minAICPSNR timeerrorsP checkwindowP minfactorP'.split(' ')
+    p_parameter_names = 'algoP pstart pstop use_taup taup_model tlta tsnrz hosorder bpz1 bpz2 pickwinP aictsmooth tsmoothP ausP nfacP tpred1z tdet1z Parorder addnoise Precalcwin minAICPslope minAICPSNR timeerrorsP checkwindowP minfactorP'.split(
-    s_parameter_names = 'algoS sstart sstop bph1 bph2 tsnrh pickwinS tpred1h tdet1h tpred2h tdet2h Sarorder aictsmoothS tsmoothS ausS minAICSslope minAICSSNR Srecalcwin nfacS timeerrorsS zfac checkwindowS minfactorS'.split(' ')
+        ' ')
+    s_parameter_names = 'algoS sstart sstop bph1 bph2 tsnrh pickwinS tpred1h tdet1h tpred2h tdet2h Sarorder aictsmoothS tsmoothS ausS minAICSslope minAICSSNR Srecalcwin nfacS timeerrorsS zfac checkwindowS minfactorS'.split(
+        ' ')
     first_motion_names = 'minFMSNR fmpickwin minfmweight'.split(' ')
     signal_length_names = 'minsiglength minpercent noisefactor'.split(' ')
     # Get list of values from pickparam by name
@@ -1490,10 +1499,11 @@ def get_pickparams(pickparam):
     first_motion_params = dict(zip(first_motion_names, fm_parameter_values))
     signal_length_params = dict(zip(signal_length_names, sl_parameter_values))
 
-    p_params['use_taup'] = get_Bool(p_params['use_taup'])
+    p_params['use_taup'] = get_bool(p_params['use_taup'])
 
     return p_params, s_params, first_motion_params, signal_length_params
 
+
 def getQualityFromUncertainty(uncertainty, Errors):
     # set initial quality to 4 (worst) and change only if one condition is hit
     quality = 4
@@ -1517,6 +1527,7 @@ def getQualityFromUncertainty(uncertainty, Errors):
 
     return quality
 
+
 if __name__ == '__main__':
     import doctest
 

pylot/core/util/array_map.py

@@ -1,27 +1,22 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 
-import sys
+import traceback
-import os
-import matplotlib
-
-from PySide2 import QtCore, QtGui, QtWidgets
-from PySide2.QtCore import Qt
-from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
-import matplotlib.patheffects as PathEffects
 
 import cartopy.crs as ccrs
-import matplotlib.pyplot as plt
 import cartopy.feature as cf
-from cartopy.mpl.gridliner import LongitudeFormatter, LatitudeFormatter
+import matplotlib
-
+import matplotlib.patheffects as PathEffects
-import traceback
+import matplotlib.pyplot as plt
-import obspy
 import numpy as np
+import obspy
+from PySide2 import QtWidgets
+from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
 from mpl_toolkits.axes_grid1.inset_locator import inset_axes
 from scipy.interpolate import griddata
-from pylot.core.util.widgets import PickDlg
+
 from pylot.core.pick.utils import get_quality_class
+from pylot.core.util.widgets import PickDlg
 
 matplotlib.use('Qt5Agg')
 
@@ -42,7 +37,7 @@ class MplCanvas(FigureCanvas):
 class Array_map(QtWidgets.QWidget):
     def __init__(self, parent, metadata, parameter=None, axes=None, annotate=True, pointsize=25.,
                  linewidth=1.5, width=5e6, height=2e6):
-        QtWidgets.QWidget.__init__(self)
+        QtWidgets.QWidget.__init__(self, parent=parent)
 
         assert (parameter is not None or parent is not None), 'either parent or parameter has to be set'
         # set properties
@@ -80,7 +75,6 @@ class Array_map(QtWidgets.QWidget):
 
         self._style = None if not hasattr(parent, '_style') else parent._style
 
-        self.show()
 
     def init_map(self):
         self.init_colormap()
@@ -173,7 +167,8 @@ class Array_map(QtWidgets.QWidget):
         self.canvas.fig.tight_layout()
 
     def add_merid_paral(self):
-        self.gridlines = self.canvas.axes.gridlines(draw_labels=False, alpha=0.6, color='gray', linewidth=self.linewidth/2, zorder=7)
+        self.gridlines = self.canvas.axes.gridlines(draw_labels=False, alpha=0.6, color='gray',
+                                                    linewidth=self.linewidth / 2, zorder=7)
         # TODO: current cartopy version does not support label removal. Devs are working on it.
         #  Should be fixed in coming cartopy versions
         # self.gridlines.xformatter = LONGITUDE_FORMATTER
@@ -468,20 +463,23 @@ class Array_map(QtWidgets.QWidget):
                                                                   transform=ccrs.PlateCarree(), label='deleted'))
 
     def openPickDlg(self, ind):
-        data = self._parent.get_data().getWFData()
+        wfdata = self._parent.get_data().getWFData()
+        wfdata_comp = self._parent.get_data().getWFDataComp()
         for index in ind:
             network, station = self._station_onpick_ids[index].split('.')[:2]
             pyl_mw = self._parent
             try:
-                data = data.select(station=station)
+                wfdata = wfdata.select(station=station)
-                if not data:
+                wfdata_comp = wfdata_comp.select(station=station)
+                if not wfdata:
                     self._warn('No data for station {}'.format(station))
                     return
                 pickDlg = PickDlg(self._parent, parameter=self.parameter,
-                                  data=data, network=network, station=station,
+                                  data=wfdata.copy(), data_compare=wfdata_comp.copy(), network=network, station=station,
                                   picks=self._parent.get_current_event().getPick(station),
                                   autopicks=self._parent.get_current_event().getAutopick(station),
                                   filteroptions=self._parent.filteroptions, metadata=self.metadata,
+                                  model=self.parameter.get('taup_model'),
                                   event=pyl_mw.get_current_event())
             except Exception as e:
                 message = 'Could not generate Plot for station {st}.\n {er}'.format(st=station, er=e)
@@ -513,7 +511,7 @@ class Array_map(QtWidgets.QWidget):
         levels = np.linspace(self.get_min_from_picks(), self.get_max_from_picks(), nlevel)
 
         self.contourf = self.canvas.axes.contourf(self.longrid, self.latgrid, self.picksgrid_active, levels,
-                                                  linewidths=self.linewidth*5, transform=ccrs.PlateCarree(),
+                                                  linewidths=self.linewidth * 5, transform=ccrs.PlateCarree(),
                                                   alpha=0.4, zorder=8, cmap=self.get_colormap())
 
     def get_colormap(self):

pylot/core/util/connection.py

@@ -2,12 +2,13 @@
 # -*- coding: utf-8 -*-
 
 try:
+    # noinspection PyUnresolvedReferences
     from urllib2 import urlopen
 except:
     from urllib.request import urlopen
 
 
-def checkurl(url='https://ariadne.geophysik.ruhr-uni-bochum.de/trac/PyLoT/'):
+def checkurl(url='https://git.geophysik.ruhr-uni-bochum.de/marcel/pylot/'):
     """
     check if URL is available
     :param url: url

pylot/core/util/dataprocessing.py

@@ -2,9 +2,10 @@
 # -*- coding: utf-8 -*-
 
 import glob
-import numpy as np
 import os
 import sys
+
+import numpy as np
 from obspy import UTCDateTime, read_inventory, read
 from obspy.io.xseed import Parser
 
@@ -46,7 +47,7 @@ class Metadata(object):
     def __repr__(self):
         return self.__str__()
 
-    def add_inventory(self, path_to_inventory, obspy_dmt_inv = False):
+    def add_inventory(self, path_to_inventory, obspy_dmt_inv=False):
         """
         Add path to list of inventories.
         :param path_to_inventory: Path to a folder
@@ -211,6 +212,7 @@ class Metadata(object):
                         self.stations_dict[st_id] = {'latitude': station[0].latitude,
                                                      'longitude': station[0].longitude,
                                                      'elevation': station[0].elevation}
+
         read_stat = {'xml': stat_info_from_inventory,
                      'dless': stat_info_from_parser}
 
@@ -269,7 +271,7 @@ class Metadata(object):
                     continue
             invtype, robj = self._read_metadata_file(os.path.join(path_to_inventory, fname))
             try:
-                robj.get_coordinates(station_seed_id)
+                # robj.get_coordinates(station_seed_id)  # TODO: Commented out, failed with Parser, is this needed?
                 self.inventory_files[fname] = {'invtype': invtype,
                                                'data': robj}
                 if station_seed_id in self.seed_ids.keys():
@@ -351,25 +353,25 @@ def check_time(datetime):
     :type datetime: list
     :return: returns True if Values are in supposed range, returns False otherwise
 
-    >>> check_time([1999, 01, 01, 23, 59, 59, 999000])
+    >>> check_time([1999, 1, 1, 23, 59, 59, 999000])
     True
-    >>> check_time([1999, 01, 01, 23, 59, 60, 999000])
+    >>> check_time([1999, 1, 1, 23, 59, 60, 999000])
     False
-    >>> check_time([1999, 01, 01, 23, 59, 59, 1000000])
+    >>> check_time([1999, 1, 1, 23, 59, 59, 1000000])
     False
-    >>> check_time([1999, 01, 01, 23, 60, 59, 999000])
+    >>> check_time([1999, 1, 1, 23, 60, 59, 999000])
     False
-    >>> check_time([1999, 01, 01, 23, 60, 59, 999000])
+    >>> check_time([1999, 1, 1, 23, 60, 59, 999000])
     False
-    >>> check_time([1999, 01, 01, 24, 59, 59, 999000])
+    >>> check_time([1999, 1, 1, 24, 59, 59, 999000])
     False
-    >>> check_time([1999, 01, 31, 23, 59, 59, 999000])
+    >>> check_time([1999, 1, 31, 23, 59, 59, 999000])
     True
-    >>> check_time([1999, 02, 30, 23, 59, 59, 999000])
+    >>> check_time([1999, 2, 30, 23, 59, 59, 999000])
     False
-    >>> check_time([1999, 02, 29, 23, 59, 59, 999000])
+    >>> check_time([1999, 2, 29, 23, 59, 59, 999000])
     False
-    >>> check_time([2000, 02, 29, 23, 59, 59, 999000])
+    >>> check_time([2000, 2, 29, 23, 59, 59, 999000])
     True
     >>> check_time([2000, 13, 29, 23, 59, 59, 999000])
     False
@@ -380,6 +382,7 @@ def check_time(datetime):
     except ValueError:
         return False
 
+
 # TODO: change root to datapath
 def get_file_list(root_dir):
     """
@@ -444,7 +447,7 @@ def evt_head_check(root_dir, out_dir=None):
     """
     if not out_dir:
         print('WARNING files are going to be overwritten!')
-        inp = str(raw_input('Continue? [y/N]'))
+        inp = str(input('Continue? [y/N]'))
         if not inp == 'y':
             sys.exit()
     filelist = get_file_list(root_dir)

pylot/core/util/defaults.py

@@ -26,9 +26,7 @@ elif system_name == "Windows":
 # suffix for phase name if not phase identified by last letter (P, p, etc.)
 ALTSUFFIX = ['diff', 'n', 'g', '1', '2', '3']
 
-FILTERDEFAULTS = readDefaultFilterInformation(os.path.join(os.path.expanduser('~'),
+FILTERDEFAULTS = readDefaultFilterInformation()
-                                                           '.pylot',
-                                                           'pylot.in'))
 
 TIMEERROR_DEFAULTS = os.path.join(os.path.expanduser('~'),
                                   '.pylot',

pylot/core/util/event.py

@@ -2,6 +2,7 @@
 # -*- coding: utf-8 -*-
 
 import os
+
 from obspy import UTCDateTime
 from obspy.core.event import Event as ObsPyEvent
 from obspy.core.event import Origin, ResourceIdentifier

pylot/core/util/generate_array_maps.py

@@ -3,6 +3,7 @@
 # small script that creates array maps for each event within a previously generated PyLoT project
 
 import os
+
 num_thread = "16"
 os.environ["OMP_NUM_THREADS"] = num_thread
 os.environ["OPENBLAS_NUM_THREADS"] = num_thread
@@ -15,6 +16,7 @@ import multiprocessing
 import sys
 import glob
 import matplotlib
+
 matplotlib.use('Qt5Agg')
 sys.path.append(os.path.join('/'.join(sys.argv[0].split('/')[:-1]), '../../..'))
 
@@ -52,7 +54,8 @@ def array_map_worker(input_dict):
     print('Working on event: {} ({}/{})'.format(eventdir, input_dict['index'] + 1, input_dict['nEvents']))
     xml_picks = glob.glob(os.path.join(eventdir, f'*{input_dict["f_ext"]}.xml'))
     if not len(xml_picks):
-        print('Event {} does not have any picks associated with event file extension {}'. format(eventdir, input_dict['f_ext']))
+        print('Event {} does not have any picks associated with event file extension {}'.format(eventdir,
+                                                                                                input_dict['f_ext']))
         return
     # check for picks
     manualpicks = event.getPicks()
@@ -92,4 +95,3 @@ if __name__ == '__main__':
 
     for infile in args.infiles:
         main(os.path.join(args.dataroot, infile), f_ext='_correlated_0.03-0.1', ncores=args.ncores)
-

pylot/core/util/gui.py

@@ -11,7 +11,6 @@ try:
 except Exception as e:
     print('Warning: Could not import module QtCore.')
 
-
 from pylot.core.util.utils import pick_color
 
 
@@ -57,7 +56,7 @@ def which(program, parameter):
         nllocpath = ":" + parameter.get('nllocbin')
         os.environ['PATH'] += nllocpath
     except Exception as e:
-        print(e.message)
+        print(e)
 
     def is_exe(fpath):
         return os.path.exists(fpath) and os.access(fpath, os.X_OK)
@@ -101,4 +100,3 @@ def make_pen(picktype, phase, key, quality):
         linestyle, width = pick_linestyle_pg(picktype, key)
         pen = pg.mkPen(rgba, width=width, style=linestyle)
         return pen
-

pylot/core/util/obspyDMT_interface.py

@@ -2,6 +2,7 @@
 # -*- coding: utf-8 -*-
 
 import os
+
 from obspy import UTCDateTime
 
 
@@ -36,12 +37,12 @@ def qml_from_obspyDMT(path):
         return IOError('Could not find Event at {}'.format(path))
 
     with open(path, 'rb') as infile:
-        event_dmt = pickle.load(infile)#, fix_imports=True)
+        event_dmt = pickle.load(infile)  # , fix_imports=True)
 
     event_dmt['origin_id'].id = str(event_dmt['origin_id'].id)
 
     ev = Event(resource_id=event_dmt['event_id'])
-    #small bugfix "unhashable type: 'newstr' "
+    # small bugfix "unhashable type: 'newstr' "
     event_dmt['origin_id'].id = str(event_dmt['origin_id'].id)
 
     origin = Origin(resource_id=event_dmt['origin_id'],

pylot/core/util/pdf.py

@@ -1,8 +1,9 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 
-import numpy as np
 import warnings
+
+import numpy as np
 from obspy import UTCDateTime
 
 from pylot.core.util.utils import fit_curve, clims

pylot/core/util/thread.py

@@ -1,6 +1,9 @@
 # -*- coding: utf-8 -*-
-import sys, os, traceback
 import multiprocessing
+import os
+import sys
+import traceback
+
 from PySide2.QtCore import QThread, Signal, Qt, Slot, QRunnable, QObject
 from PySide2.QtWidgets import QDialog, QProgressBar, QLabel, QHBoxLayout, QPushButton
 
@@ -19,9 +22,11 @@ class Thread(QThread):
         self.abortButton = abortButton
         self.finished.connect(self.hideProgressbar)
         self.showProgressbar()
+        self.old_stdout = None
 
     def run(self):
         if self.redirect_stdout:
+            self.old_stdout = sys.stdout
             sys.stdout = self
         try:
             if self.arg is not None:
@@ -36,7 +41,8 @@ class Thread(QThread):
             exctype, value = sys.exc_info()[:2]
             self._executedErrorInfo = '{} {} {}'. \
                 format(exctype, value, traceback.format_exc())
-        sys.stdout = sys.__stdout__
+        if self.redirect_stdout:
+            sys.stdout = self.old_stdout
 
     def showProgressbar(self):
         if self.progressText:
@@ -93,23 +99,25 @@ class Worker(QRunnable):
         self.progressText = progressText
         self.pb_widget = pb_widget
         self.redirect_stdout = redirect_stdout
+        self.old_stdout = None
 
     @Slot()
     def run(self):
         if self.redirect_stdout:
+            self.old_stdout = sys.stdout
             sys.stdout = self
 
         try:
             result = self.fun(self.args)
         except:
-            exctype, value = sys.exc_info ()[:2]
+            exctype, value = sys.exc_info()[:2]
             print(exctype, value, traceback.format_exc())
-            self.signals.error.emit ((exctype, value, traceback.format_exc ()))
+            self.signals.error.emit((exctype, value, traceback.format_exc()))
         else:
             self.signals.result.emit(result)
         finally:
             self.signals.finished.emit('Done')
-        sys.stdout = sys.__stdout__
+        sys.stdout = self.old_stdout
 
     def write(self, text):
         self.signals.message.emit(text)
@@ -141,11 +149,13 @@ class MultiThread(QThread):
         self.progressText = progressText
         self.pb_widget = pb_widget
         self.redirect_stdout = redirect_stdout
+        self.old_stdout = None
         self.finished.connect(self.hideProgressbar)
         self.showProgressbar()
 
     def run(self):
         if self.redirect_stdout:
+            self.old_stdout = sys.stdout
             sys.stdout = self
         try:
             if not self.ncores:
@@ -161,7 +171,7 @@ class MultiThread(QThread):
             exc_type, exc_obj, exc_tb = sys.exc_info()
             fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
             print('Exception: {}, file: {}, line: {}'.format(exc_type, fname, exc_tb.tb_lineno))
-        sys.stdout = sys.__stdout__
+        sys.stdout = self.old_stdout
 
     def showProgressbar(self):
         if self.progressText:

pylot/core/util/utils.py

@@ -2,12 +2,13 @@
 # -*- coding: utf-8 -*-
 
 import hashlib
-import numpy as np
 import os
 import platform
 import re
 import subprocess
 import warnings
+
+import numpy as np
 from obspy import UTCDateTime, read
 from obspy.core import AttribDict
 from obspy.signal.rotate import rotate2zne
@@ -36,15 +37,14 @@ def getAutoFilteroptions(phase, parameter):
     return filteroptions
 
 
-def readDefaultFilterInformation(fname):
+def readDefaultFilterInformation():
     """
     Read default filter information from pylot.in file
-    :param fname: path to pylot.in file
-    :type fname: str
     :return: dictionary containing the defailt filter information
     :rtype: dict
     """
-    pparam = PylotParameter(fname)
+    pparam = PylotParameter()
+    pparam.reset_defaults()
     return readFilterInformation(pparam)
 
 
@@ -327,7 +327,7 @@ def get_None(value):
         return value
 
 
-def get_Bool(value):
+def get_bool(value):
     """
     Convert string representations of bools to their true boolean value
     :param value:
@@ -335,13 +335,14 @@ def get_Bool(value):
     :return: true boolean value
     :rtype: bool
     """
-    if value in ['True', 'true']:
+    if type(value) is bool:
+        return value
+    elif value in ['True', 'true']:
         return True
     elif value in ['False', 'false']:
         return False
     else:
-        return value
+        return bool(value)
-
 
 def four_digits(year):
     """
@@ -1168,7 +1169,7 @@ def correct_iplot(iplot):
     try:
         iplot = int(iplot)
     except ValueError:
-        if get_Bool(iplot):
+        if get_bool(iplot):
             iplot = 2
         else:
             iplot = 0

pylot/core/util/version.py

@@ -35,9 +35,9 @@ from __future__ import print_function
 
 __all__ = "get_git_version"
 
+import inspect
 # NO IMPORTS FROM PYLOT IN THIS FILE! (file gets used at installation time)
 import os
-import inspect
 from subprocess import Popen, PIPE
 
 # NO IMPORTS FROM PYLOT IN THIS FILE! (file gets used at installation time)

requirements.txt

@@ -0,0 +1,12 @@
+# This file may be used to create an environment using:
+# $ conda create --name <env> --file <this file>
+# platform: win-64
+cartopy=0.20.2
+matplotlib-base=3.3.4
+numpy=1.22.3
+obspy=1.3.0
+pyqtgraph=0.12.4
+pyside2=5.13.2
+python=3.8.12
+qt=5.12.9
+scipy=1.8.0

setup.py

@@ -1,17 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-from distutils.core import setup
-
-setup(
-    name='PyLoT',
-    version='0.2',
-    packages=['pylot', 'pylot.core', 'pylot.core.loc', 'pylot.core.pick',
-              'pylot.core.io', 'pylot.core.util', 'pylot.core.active',
-              'pylot.core.analysis', 'pylot.testing'],
-    requires=['obspy', 'PySide2', 'matplotlib', 'numpy', 'scipy', 'pyqtgraph', 'cartopy'],
-    url='dummy',
-    license='LGPLv3',
-    author='Sebastian Wehling-Benatelli',
-    author_email='sebastian.wehling@rub.de',
-    description='Comprehensive Python picking and Location Toolbox for seismological data.'
-)

tests/testPickingResults.py

@@ -1,6 +1,8 @@
 import unittest
+
 from pylot.core.pick.autopick import PickingResults
 
+
 class TestPickingResults(unittest.TestCase):
 
     def setUp(self):
@@ -70,9 +72,9 @@ class TestPickingResults(unittest.TestCase):
             curr_len = len(self.pr)
         except Exception:
             self.fail("test_dunder_attributes overwrote an instance internal dunder method")
-        self.assertEqual(prev_len+1, curr_len)        # +1 for the added __len__ key/value-pair
+        self.assertEqual(prev_len + 1, curr_len)  # +1 for the added __len__ key/value-pair
 
         self.pr.__len__ = 42
 
         self.assertEqual(42, self.pr['__len__'])
-        self.assertEqual(prev_len+1, curr_len, msg="__len__ was overwritten")
+        self.assertEqual(prev_len + 1, curr_len, msg="__len__ was overwritten")

tests/test_Metadata/test_Metadata.py

@@ -1,5 +1,6 @@
 import os
 import unittest
+
 from obspy import UTCDateTime
 from obspy.io.xseed import Parser
 from obspy.io.xseed.utils import SEEDParserException
@@ -27,7 +28,7 @@ class TestMetadata(unittest.TestCase):
         result = {}
         for channel in ('Z', 'N', 'E'):
             with HidePrints():
-                coords = self.m.get_coordinates(self.station_id+channel, time=self.time)
+                coords = self.m.get_coordinates(self.station_id + channel, time=self.time)
             result[channel] = coords
             self.assertDictEqual(result[channel], expected[channel])
 
@@ -42,7 +43,7 @@ class TestMetadata(unittest.TestCase):
         result = {}
         for channel in ('Z', 'N', 'E'):
             with HidePrints():
-                coords = self.m.get_coordinates(self.station_id+channel)
+                coords = self.m.get_coordinates(self.station_id + channel)
             result[channel] = coords
             self.assertDictEqual(result[channel], expected[channel])
 
@@ -145,7 +146,7 @@ class TestMetadata_read_single_file(unittest.TestCase):
 
     def test_read_single_file(self):
         """Test if reading a single file works"""
-        fname = os.path.join(self.metadata_folders[0], 'DATALESS.'+self.station_id)
+        fname = os.path.join(self.metadata_folders[0], 'DATALESS.' + self.station_id)
         with HidePrints():
             res = self.m.read_single_file(fname)
         # method should return true if file is successfully read
@@ -172,7 +173,7 @@ class TestMetadata_read_single_file(unittest.TestCase):
 
     def test_read_single_file_multiple_times(self):
         """Test if reading a file twice doesnt add it twice to the metadata object"""
-        fname = os.path.join(self.metadata_folders[0], 'DATALESS.'+self.station_id)
+        fname = os.path.join(self.metadata_folders[0], 'DATALESS.' + self.station_id)
         with HidePrints():
             res1 = self.m.read_single_file(fname)
             res2 = self.m.read_single_file(fname)
@@ -197,7 +198,8 @@ class TestMetadataMultipleTime(unittest.TestCase):
     def setUp(self):
         self.seed_id = 'LE.ROTT..HN'
         path = os.path.dirname(__file__)  # gets path to currently running script
-        metadata = os.path.join('test_data', 'dless_multiple_times', 'MAGS2_LE_ROTT.dless')  # specific subfolder of test data
+        metadata = os.path.join('test_data', 'dless_multiple_times',
+                                'MAGS2_LE_ROTT.dless')  # specific subfolder of test data
         metadata_path = os.path.join(path, metadata)
         self.m = Metadata(metadata_path)
         self.p = Parser(metadata_path)
@@ -299,7 +301,8 @@ Channels:
     def setUp(self):
         self.seed_id = 'KB.TMO07.00.HHZ'
         path = os.path.dirname(__file__)  # gets path to currently running script
-        metadata = os.path.join('test_data', 'dless_multiple_instruments', 'MAGS2_KB_TMO07.dless')  # specific subfolder of test data
+        metadata = os.path.join('test_data', 'dless_multiple_instruments',
+                                'MAGS2_KB_TMO07.dless')  # specific subfolder of test data
         metadata_path = os.path.join(path, metadata)
         self.m = Metadata(metadata_path)
         self.p = Parser(metadata_path)

tests/test_PickingParameters.py

@@ -1,35 +0,0 @@
-import unittest
-from pylot.core.pick.autopick import PickingParameters
-
-class TestPickingParameters(unittest.TestCase):
-
-    def setUp(self):
-        self.simple_dict = {'a': 3, 'b': 14}
-        self.nested_dict = {'a': self.simple_dict, 'b': self.simple_dict}
-
-    def assertParameterEquality(self, dic, instance):
-        """Test wether all parameters given in dic are found in instance"""
-        for key, value in dic.items():
-            self.assertEqual(value, getattr(instance, key))
-
-    def test_add_params_from_dict_simple(self):
-        pickparam = PickingParameters()
-        pickparam.add_params_from_dict(self.simple_dict)
-        self.assertParameterEquality(self.simple_dict, pickparam)
-
-    def test_add_params_from_dict_nested(self):
-        pickparam = PickingParameters()
-        pickparam.add_params_from_dict(self.nested_dict)
-        self.assertParameterEquality(self.nested_dict, pickparam)
-
-    def test_init(self):
-        pickparam = PickingParameters(self.simple_dict)
-        self.assertParameterEquality(self.simple_dict, pickparam)
-
-    def test_dot_access(self):
-        pickparam = PickingParameters(self.simple_dict)
-        self.assertEqual(pickparam.a, self.simple_dict['a'])
-
-
-if __name__ == '__main__':
-    unittest.main()

tests/test_autopickstation/e0001.024.16/PyLoT_e0001.024.16.xml

@@ -1,21 +1,21 @@
 <?xml version='1.0' encoding='utf-8'?>
 <q:quakeml xmlns:q="http://quakeml.org/xmlns/quakeml/1.2" xmlns="http://quakeml.org/xmlns/bed/1.2">
-  <eventParameters publicID="smi:local/53a38563-739a-48b2-9f34-bf40ee7b656a">
+    <eventParameters publicID="smi:local/53a38563-739a-48b2-9f34-bf40ee7b656a">
-    <event publicID="smi:local/e0001.024.16">
+        <event publicID="smi:local/e0001.024.16">
-      <origin publicID="smi:local/e0001.024.16">
+            <origin publicID="smi:local/e0001.024.16">
-        <time>
+                <time>
-          <value>2016-01-24T10:30:30.000000Z</value>
+                    <value>2016-01-24T10:30:30.000000Z</value>
-        </time>
+                </time>
-        <latitude>
+                <latitude>
-          <value>59.66</value>
+                    <value>59.66</value>
-        </latitude>
+                </latitude>
-        <longitude>
+                <longitude>
-          <value>-153.45</value>
+                    <value>-153.45</value>
-        </longitude>
+                </longitude>
-        <depth>
+                <depth>
-          <value>128.0</value>
+                    <value>128.0</value>
-        </depth>
+                </depth>
-      </origin>
+            </origin>
-    </event>
+        </event>
-  </eventParameters>
+    </eventParameters>
 </q:quakeml>

tests/test_autopickstation/test_autopickstation.py

@@ -1,12 +1,13 @@
-import unittest
-from unittest import skip
-import obspy
-from obspy import UTCDateTime
 import os
 import sys
-from pylot.core.pick.autopick import autopickstation
+import unittest
-from pylot.core.io.inputs import PylotParameter
+
+import obspy
+from obspy import UTCDateTime
+
 from pylot.core.io.data import Data
+from pylot.core.io.inputs import PylotParameter
+from pylot.core.pick.autopick import autopickstation
 from pylot.core.util.utils import trim_station_components
 
 
@@ -93,51 +94,100 @@ class TestAutopickStation(unittest.TestCase):
         self.inputfile_taupy_disabled = os.path.join(os.path.dirname(__file__), 'autoPyLoT_global_taupy_false.in')
         self.pickparam_taupy_enabled = PylotParameter(fnin=self.inputfile_taupy_enabled)
         self.pickparam_taupy_disabled = PylotParameter(fnin=self.inputfile_taupy_disabled)
-        self.xml_file = os.path.join(os.path.dirname(__file__),self.event_id, 'PyLoT_'+self.event_id+'.xml')
+        self.xml_file = os.path.join(os.path.dirname(__file__), self.event_id, 'PyLoT_' + self.event_id + '.xml')
         self.data = Data(evtdata=self.xml_file)
         # create origin for taupy testing
-        self.origin = [obspy.core.event.origin.Origin(magnitude=7.1, latitude=59.66, longitude=-153.45, depth=128.0, time=UTCDateTime("2016-01-24T10:30:30.0"))]
+        self.origin = [obspy.core.event.origin.Origin(magnitude=7.1, latitude=59.66, longitude=-153.45, depth=128.0,
+                                                      time=UTCDateTime("2016-01-24T10:30:30.0"))]
         # mocking metadata since reading it takes a long time to read from file
         self.metadata = MockMetadata()
 
         # show complete diff when difference in results dictionaries are found
         self.maxDiff = None
 
-    #@skip("Works")
+    # @skip("Works")
     def test_autopickstation_taupy_disabled_gra1(self):
-        expected = {'P': {'picker': 'auto', 'snrdb': 15.405649120980094, 'weight': 0, 'Mo': None, 'marked': [], 'Mw': None, 'fc': None, 'snr': 34.718816470730317, 'mpp': UTCDateTime(2016, 1, 24, 10, 41, 31, 690000), 'w0': None, 'spe': 0.93333333333333235, 'network': u'GR', 'epp': UTCDateTime(2016, 1, 24, 10, 41, 28, 890000), 'lpp': UTCDateTime(2016, 1, 24, 10, 41, 32, 690000), 'fm': 'D', 'channel': u'LHZ'}, 'S': {'picker': 'auto', 'snrdb': 10.669661906545489, 'network': u'GR', 'weight': 0, 'Ao': None, 'lpp': UTCDateTime(2016, 1, 24, 10, 50, 30, 690000), 'snr': 11.667187857573905, 'epp': UTCDateTime(2016, 1, 24, 10, 50, 21, 690000), 'mpp': UTCDateTime(2016, 1, 24, 10, 50, 29, 690000), 'fm': None, 'spe': 2.6666666666666665, 'channel': u'LHE'}}
+        expected = {
+            'P': {'picker': 'auto', 'snrdb': 15.405649120980094, 'weight': 0, 'Mo': None, 'marked': [], 'Mw': None,
+                  'fc': None, 'snr': 34.718816470730317, 'mpp': UTCDateTime(2016, 1, 24, 10, 41, 31, 690000),
+                  'w0': None, 'spe': 0.93333333333333235, 'network': u'GR',
+                  'epp': UTCDateTime(2016, 1, 24, 10, 41, 28, 890000),
+                  'lpp': UTCDateTime(2016, 1, 24, 10, 41, 32, 690000), 'fm': 'D', 'channel': u'LHZ'},
+            'S': {'picker': 'auto', 'snrdb': 10.669661906545489, 'network': u'GR', 'weight': 0, 'Ao': None,
+                  'lpp': UTCDateTime(2016, 1, 24, 10, 50, 30, 690000), 'snr': 11.667187857573905,
+                  'epp': UTCDateTime(2016, 1, 24, 10, 50, 21, 690000),
+                  'mpp': UTCDateTime(2016, 1, 24, 10, 50, 29, 690000), 'fm': None, 'spe': 2.6666666666666665,
+                  'channel': u'LHE'}}
         with HidePrints():
-            result, station = autopickstation(wfstream=self.gra1, pickparam=self.pickparam_taupy_disabled, metadata=(None, None))
+            result, station = autopickstation(wfstream=self.gra1, pickparam=self.pickparam_taupy_disabled,
+                                              metadata=(None, None))
         self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
         self.assertDictContainsSubset(expected=expected['S'], actual=result['S'])
         self.assertEqual('GRA1', station)
 
     def test_autopickstation_taupy_enabled_gra1(self):
-        expected = {'P': {'picker': 'auto', 'snrdb': 15.599905299126778, 'weight': 0, 'Mo': None, 'marked': [], 'Mw': None, 'fc': None, 'snr': 36.307013769185403, 'mpp': UTCDateTime(2016, 1, 24, 10, 41, 27, 690000), 'w0': None, 'spe': 0.93333333333333235, 'network': u'GR', 'epp': UTCDateTime(2016, 1, 24, 10, 41, 24, 890000), 'lpp': UTCDateTime(2016, 1, 24, 10, 41, 28, 690000), 'fm': 'U', 'channel': u'LHZ'}, 'S': {'picker': 'auto', 'snrdb': 10.669661906545489, 'network': u'GR', 'weight': 0, 'Ao': None, 'lpp': UTCDateTime(2016, 1, 24, 10, 50, 30, 690000), 'snr': 11.667187857573905, 'epp': UTCDateTime(2016, 1, 24, 10, 50, 21, 690000), 'mpp': UTCDateTime(2016, 1, 24, 10, 50, 29, 690000), 'fm': None, 'spe': 2.6666666666666665, 'channel': u'LHE'}}
+        expected = {
+            'P': {'picker': 'auto', 'snrdb': 15.599905299126778, 'weight': 0, 'Mo': None, 'marked': [], 'Mw': None,
+                  'fc': None, 'snr': 36.307013769185403, 'mpp': UTCDateTime(2016, 1, 24, 10, 41, 27, 690000),
+                  'w0': None, 'spe': 0.93333333333333235, 'network': u'GR',
+                  'epp': UTCDateTime(2016, 1, 24, 10, 41, 24, 890000),
+                  'lpp': UTCDateTime(2016, 1, 24, 10, 41, 28, 690000), 'fm': 'U', 'channel': u'LHZ'},
+            'S': {'picker': 'auto', 'snrdb': 10.669661906545489, 'network': u'GR', 'weight': 0, 'Ao': None,
+                  'lpp': UTCDateTime(2016, 1, 24, 10, 50, 30, 690000), 'snr': 11.667187857573905,
+                  'epp': UTCDateTime(2016, 1, 24, 10, 50, 21, 690000),
+                  'mpp': UTCDateTime(2016, 1, 24, 10, 50, 29, 690000), 'fm': None, 'spe': 2.6666666666666665,
+                  'channel': u'LHE'}}
         with HidePrints():
-            result, station = autopickstation(wfstream=self.gra1, pickparam=self.pickparam_taupy_enabled, metadata=self.metadata, origin=self.origin)
+            result, station = autopickstation(wfstream=self.gra1, pickparam=self.pickparam_taupy_enabled,
+                                              metadata=self.metadata, origin=self.origin)
         self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
         self.assertDictContainsSubset(expected=expected['S'], actual=result['S'])
         self.assertEqual('GRA1', station)
 
     def test_autopickstation_taupy_disabled_gra2(self):
-        expected = {'P': {'picker': 'auto', 'snrdb': None, 'weight': 9, 'Mo': None, 'marked': 'shortsignallength', 'Mw': None, 'fc': None, 'snr': None, 'mpp': UTCDateTime(2016, 1, 24, 10, 36, 59, 150000), 'w0': None, 'spe': None, 'network': u'GR', 'epp': UTCDateTime(2016, 1, 24, 10, 36, 43, 150000), 'lpp': UTCDateTime(2016, 1, 24, 10, 37, 15, 150000), 'fm': 'N', 'channel': u'LHZ'}, 'S': {'picker': 'auto', 'snrdb': None, 'network': u'GR', 'weight': 4, 'Ao': None, 'lpp': UTCDateTime(2016, 1, 24, 10, 37, 15, 150000), 'snr': None, 'epp': UTCDateTime(2016, 1, 24, 10, 36, 43, 150000), 'mpp': UTCDateTime(2016, 1, 24, 10, 36, 59, 150000), 'fm': None, 'spe': None, 'channel': u'LHE'}}
+        expected = {
+            'P': {'picker': 'auto', 'snrdb': None, 'weight': 9, 'Mo': None, 'marked': 'shortsignallength', 'Mw': None,
+                  'fc': None, 'snr': None, 'mpp': UTCDateTime(2016, 1, 24, 10, 36, 59, 150000), 'w0': None, 'spe': None,
+                  'network': u'GR', 'epp': UTCDateTime(2016, 1, 24, 10, 36, 43, 150000),
+                  'lpp': UTCDateTime(2016, 1, 24, 10, 37, 15, 150000), 'fm': 'N', 'channel': u'LHZ'},
+            'S': {'picker': 'auto', 'snrdb': None, 'network': u'GR', 'weight': 4, 'Ao': None,
+                  'lpp': UTCDateTime(2016, 1, 24, 10, 37, 15, 150000), 'snr': None,
+                  'epp': UTCDateTime(2016, 1, 24, 10, 36, 43, 150000),
+                  'mpp': UTCDateTime(2016, 1, 24, 10, 36, 59, 150000), 'fm': None, 'spe': None, 'channel': u'LHE'}}
         with HidePrints():
-            result, station = autopickstation(wfstream=self.gra2, pickparam=self.pickparam_taupy_disabled, metadata=(None, None))
+            result, station = autopickstation(wfstream=self.gra2, pickparam=self.pickparam_taupy_disabled,
+                                              metadata=(None, None))
         self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
         self.assertDictContainsSubset(expected=expected['S'], actual=result['S'])
         self.assertEqual('GRA2', station)
 
     def test_autopickstation_taupy_enabled_gra2(self):
-        expected = {'P': {'picker': 'auto', 'snrdb': 13.957959025719253, 'weight': 0, 'Mo': None, 'marked': [], 'Mw': None, 'fc': None, 'snr': 24.876879503607871, 'mpp': UTCDateTime(2016, 1, 24, 10, 41, 29, 150000), 'w0': None, 'spe': 1.0, 'network': u'GR', 'epp': UTCDateTime(2016, 1, 24, 10, 41, 26, 150000), 'lpp': UTCDateTime(2016, 1, 24, 10, 41, 30, 150000), 'fm': None, 'channel': u'LHZ'}, 'S': {'picker': 'auto', 'snrdb': 10.573236990555648, 'network': u'GR', 'weight': 1, 'Ao': None, 'lpp': UTCDateTime(2016, 1, 24, 10, 50, 34, 150000), 'snr': 11.410999834108294, 'epp': UTCDateTime(2016, 1, 24, 10, 50, 21, 150000), 'mpp': UTCDateTime(2016, 1, 24, 10, 50, 33, 150000), 'fm': None, 'spe': 4.666666666666667, 'channel': u'LHE'}}
+        expected = {
+            'P': {'picker': 'auto', 'snrdb': 13.957959025719253, 'weight': 0, 'Mo': None, 'marked': [], 'Mw': None,
+                  'fc': None, 'snr': 24.876879503607871, 'mpp': UTCDateTime(2016, 1, 24, 10, 41, 29, 150000),
+                  'w0': None, 'spe': 1.0, 'network': u'GR', 'epp': UTCDateTime(2016, 1, 24, 10, 41, 26, 150000),
+                  'lpp': UTCDateTime(2016, 1, 24, 10, 41, 30, 150000), 'fm': None, 'channel': u'LHZ'},
+            'S': {'picker': 'auto', 'snrdb': 10.573236990555648, 'network': u'GR', 'weight': 1, 'Ao': None,
+                  'lpp': UTCDateTime(2016, 1, 24, 10, 50, 34, 150000), 'snr': 11.410999834108294,
+                  'epp': UTCDateTime(2016, 1, 24, 10, 50, 21, 150000),
+                  'mpp': UTCDateTime(2016, 1, 24, 10, 50, 33, 150000), 'fm': None, 'spe': 4.666666666666667,
+                  'channel': u'LHE'}}
         with HidePrints():
-            result, station = autopickstation(wfstream=self.gra2, pickparam=self.pickparam_taupy_enabled, metadata=self.metadata, origin = self.origin)
+            result, station = autopickstation(wfstream=self.gra2, pickparam=self.pickparam_taupy_enabled,
+                                              metadata=self.metadata, origin=self.origin)
         self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
         self.assertDictContainsSubset(expected=expected['S'], actual=result['S'])
         self.assertEqual('GRA2', station)
 
     def test_autopickstation_taupy_disabled_ech(self):
-        expected = {'P': {'picker': 'auto', 'snrdb': None, 'weight': 9, 'Mo': None, 'marked': 'SinsteadP', 'Mw': None, 'fc': None, 'snr': None, 'mpp': UTCDateTime(2016, 1, 24, 10, 26, 57), 'w0': None, 'spe': None, 'network': u'G', 'epp': UTCDateTime(2016, 1, 24, 10, 26, 41), 'lpp': UTCDateTime(2016, 1, 24, 10, 27, 13), 'fm': 'N', 'channel': u'LHZ'}, 'S': {'picker': 'auto', 'snrdb': None, 'network': u'G', 'weight': 4, 'Ao': None, 'lpp': UTCDateTime(2016, 1, 24, 10, 27, 13), 'snr': None, 'epp': UTCDateTime(2016, 1, 24, 10, 26, 41), 'mpp': UTCDateTime(2016, 1, 24, 10, 26, 57), 'fm': None, 'spe': None, 'channel': u'LHE'}}
+        expected = {'P': {'picker': 'auto', 'snrdb': None, 'weight': 9, 'Mo': None, 'marked': 'SinsteadP', 'Mw': None,
+                          'fc': None, 'snr': None, 'mpp': UTCDateTime(2016, 1, 24, 10, 26, 57), 'w0': None, 'spe': None,
+                          'network': u'G', 'epp': UTCDateTime(2016, 1, 24, 10, 26, 41),
+                          'lpp': UTCDateTime(2016, 1, 24, 10, 27, 13), 'fm': 'N', 'channel': u'LHZ'},
+                    'S': {'picker': 'auto', 'snrdb': None, 'network': u'G', 'weight': 4, 'Ao': None,
+                          'lpp': UTCDateTime(2016, 1, 24, 10, 27, 13), 'snr': None,
+                          'epp': UTCDateTime(2016, 1, 24, 10, 26, 41), 'mpp': UTCDateTime(2016, 1, 24, 10, 26, 57),
+                          'fm': None, 'spe': None, 'channel': u'LHE'}}
         with HidePrints():
             result, station = autopickstation(wfstream=self.ech, pickparam=self.pickparam_taupy_disabled)
         self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
@@ -146,16 +196,32 @@ class TestAutopickStation(unittest.TestCase):
 
     def test_autopickstation_taupy_enabled_ech(self):
         # this station has a long time of before the first onset, so taupy will help during picking
-        expected = {'P': {'picker': 'auto', 'snrdb': 9.9753586609166316, 'weight': 0, 'Mo': None, 'marked': [], 'Mw': None, 'fc': None, 'snr': 9.9434218804137107, 'mpp': UTCDateTime(2016, 1, 24, 10, 41, 34), 'w0': None, 'spe': 1.6666666666666667, 'network': u'G', 'epp': UTCDateTime(2016, 1, 24, 10, 41, 29), 'lpp': UTCDateTime(2016, 1, 24, 10, 41, 35), 'fm': None, 'channel': u'LHZ'}, 'S': {'picker': 'auto', 'snrdb': 12.698999454169567, 'network': u'G', 'weight': 0, 'Ao': None, 'lpp': UTCDateTime(2016, 1, 24, 10, 50, 44), 'snr': 18.616581906366577, 'epp': UTCDateTime(2016, 1, 24, 10, 50, 33), 'mpp': UTCDateTime(2016, 1, 24, 10, 50, 43), 'fm': None, 'spe': 3.3333333333333335, 'channel': u'LHE'}}
+        expected = {
+            'P': {'picker': 'auto', 'snrdb': 9.9753586609166316, 'weight': 0, 'Mo': None, 'marked': [], 'Mw': None,
+                  'fc': None, 'snr': 9.9434218804137107, 'mpp': UTCDateTime(2016, 1, 24, 10, 41, 34), 'w0': None,
+                  'spe': 1.6666666666666667, 'network': u'G', 'epp': UTCDateTime(2016, 1, 24, 10, 41, 29),
+                  'lpp': UTCDateTime(2016, 1, 24, 10, 41, 35), 'fm': None, 'channel': u'LHZ'},
+            'S': {'picker': 'auto', 'snrdb': 12.698999454169567, 'network': u'G', 'weight': 0, 'Ao': None,
+                  'lpp': UTCDateTime(2016, 1, 24, 10, 50, 44), 'snr': 18.616581906366577,
+                  'epp': UTCDateTime(2016, 1, 24, 10, 50, 33), 'mpp': UTCDateTime(2016, 1, 24, 10, 50, 43), 'fm': None,
+                  'spe': 3.3333333333333335, 'channel': u'LHE'}}
         with HidePrints():
-            result, station = autopickstation(wfstream=self.ech, pickparam=self.pickparam_taupy_enabled, metadata=self.metadata, origin=self.origin)
+            result, station = autopickstation(wfstream=self.ech, pickparam=self.pickparam_taupy_enabled,
+                                              metadata=self.metadata, origin=self.origin)
         self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
         self.assertDictContainsSubset(expected=expected['S'], actual=result['S'])
         self.assertEqual('ECH', station)
 
     def test_autopickstation_taupy_disabled_fiesa(self):
         # this station has a long time of before the first onset, so taupy will help during picking
-        expected = {'P': {'picker': 'auto', 'snrdb': None, 'weight': 9, 'Mo': None, 'marked': 'SinsteadP', 'Mw': None, 'fc': None, 'snr': None, 'mpp': UTCDateTime(2016, 1, 24, 10, 35, 58), 'w0': None, 'spe': None, 'network': u'CH', 'epp': UTCDateTime(2016, 1, 24, 10, 35, 42), 'lpp': UTCDateTime(2016, 1, 24, 10, 36, 14), 'fm': 'N', 'channel': u'LHZ'}, 'S': {'picker': 'auto', 'snrdb': None, 'network': u'CH', 'weight': 4, 'Ao': None, 'lpp': UTCDateTime(2016, 1, 24, 10, 36, 14), 'snr': None, 'epp': UTCDateTime(2016, 1, 24, 10, 35, 42), 'mpp': UTCDateTime(2016, 1, 24, 10, 35, 58), 'fm': None, 'spe': None, 'channel': u'LHE'}}
+        expected = {'P': {'picker': 'auto', 'snrdb': None, 'weight': 9, 'Mo': None, 'marked': 'SinsteadP', 'Mw': None,
+                          'fc': None, 'snr': None, 'mpp': UTCDateTime(2016, 1, 24, 10, 35, 58), 'w0': None, 'spe': None,
+                          'network': u'CH', 'epp': UTCDateTime(2016, 1, 24, 10, 35, 42),
+                          'lpp': UTCDateTime(2016, 1, 24, 10, 36, 14), 'fm': 'N', 'channel': u'LHZ'},
+                    'S': {'picker': 'auto', 'snrdb': None, 'network': u'CH', 'weight': 4, 'Ao': None,
+                          'lpp': UTCDateTime(2016, 1, 24, 10, 36, 14), 'snr': None,
+                          'epp': UTCDateTime(2016, 1, 24, 10, 35, 42), 'mpp': UTCDateTime(2016, 1, 24, 10, 35, 58),
+                          'fm': None, 'spe': None, 'channel': u'LHE'}}
         with HidePrints():
             result, station = autopickstation(wfstream=self.fiesa, pickparam=self.pickparam_taupy_disabled)
         self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
@@ -164,9 +230,18 @@ class TestAutopickStation(unittest.TestCase):
 
     def test_autopickstation_taupy_enabled_fiesa(self):
         # this station has a long time of before the first onset, so taupy will help during picking
-        expected = {'P': {'picker': 'auto', 'snrdb': 13.921049277904373, 'weight': 0, 'Mo': None, 'marked': [], 'Mw': None, 'fc': None, 'snr': 24.666352170589487, 'mpp': UTCDateTime(2016, 1, 24, 10, 41, 47), 'w0': None, 'spe': 1.2222222222222285, 'network': u'CH', 'epp': UTCDateTime(2016, 1, 24, 10, 41, 43, 333333), 'lpp': UTCDateTime(2016, 1, 24, 10, 41, 48), 'fm': None, 'channel': u'LHZ'}, 'S': {'picker': 'auto', 'snrdb': 10.893086316477728, 'network': u'CH', 'weight': 0, 'Ao': None, 'lpp': UTCDateTime(2016, 1, 24, 10, 51, 5), 'snr': 12.283118216397849, 'epp': UTCDateTime(2016, 1, 24, 10, 50, 59, 333333), 'mpp': UTCDateTime(2016, 1, 24, 10, 51, 2), 'fm': None, 'spe': 2.8888888888888764, 'channel': u'LHE'}}
+        expected = {
+            'P': {'picker': 'auto', 'snrdb': 13.921049277904373, 'weight': 0, 'Mo': None, 'marked': [], 'Mw': None,
+                  'fc': None, 'snr': 24.666352170589487, 'mpp': UTCDateTime(2016, 1, 24, 10, 41, 47), 'w0': None,
+                  'spe': 1.2222222222222285, 'network': u'CH', 'epp': UTCDateTime(2016, 1, 24, 10, 41, 43, 333333),
+                  'lpp': UTCDateTime(2016, 1, 24, 10, 41, 48), 'fm': None, 'channel': u'LHZ'},
+            'S': {'picker': 'auto', 'snrdb': 10.893086316477728, 'network': u'CH', 'weight': 0, 'Ao': None,
+                  'lpp': UTCDateTime(2016, 1, 24, 10, 51, 5), 'snr': 12.283118216397849,
+                  'epp': UTCDateTime(2016, 1, 24, 10, 50, 59, 333333), 'mpp': UTCDateTime(2016, 1, 24, 10, 51, 2),
+                  'fm': None, 'spe': 2.8888888888888764, 'channel': u'LHE'}}
         with HidePrints():
-            result, station = autopickstation(wfstream=self.fiesa, pickparam=self.pickparam_taupy_enabled, metadata=self.metadata, origin=self.origin)
+            result, station = autopickstation(wfstream=self.fiesa, pickparam=self.pickparam_taupy_enabled,
+                                              metadata=self.metadata, origin=self.origin)
         self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
         self.assertDictContainsSubset(expected=expected['S'], actual=result['S'])
         self.assertEqual('FIESA', station)
@@ -176,7 +251,8 @@ class TestAutopickStation(unittest.TestCase):
         wfstream = self.gra1.copy()
         wfstream = wfstream.select(channel='*E') + wfstream.select(channel='*N')
         with HidePrints():
-            result, station = autopickstation(wfstream=wfstream, pickparam=self.pickparam_taupy_disabled, metadata=(None, None))
+            result, station = autopickstation(wfstream=wfstream, pickparam=self.pickparam_taupy_disabled,
+                                              metadata=(None, None))
         self.assertIsNone(result)
         self.assertEqual('GRA1', station)
 
@@ -184,17 +260,36 @@ class TestAutopickStation(unittest.TestCase):
         """Picking on a stream without horizontal traces should still pick the P phase on the vertical component"""
         wfstream = self.gra1.copy()
         wfstream = wfstream.select(channel='*Z')
-        expected =  {'P': {'picker': 'auto', 'snrdb': 15.405649120980094, 'network': u'GR', 'weight': 0, 'Ao': None, 'Mo': None, 'marked': [], 'lpp': UTCDateTime(2016, 1, 24, 10, 41, 32, 690000), 'Mw': None, 'fc': None, 'snr': 34.718816470730317, 'epp': UTCDateTime(2016, 1, 24, 10, 41, 28, 890000), 'mpp': UTCDateTime(2016, 1, 24, 10, 41, 31, 690000), 'w0': None, 'spe': 0.9333333333333323, 'fm': 'D', 'channel': u'LHZ'}, 'S': {'picker': 'auto', 'snrdb': None, 'network': None, 'weight': 4, 'Mo': None, 'Ao': None, 'lpp': None, 'Mw': None, 'fc': None, 'snr': None, 'marked': [], 'mpp': None, 'w0': None, 'spe': None, 'epp': None, 'fm': 'N', 'channel': None}}
+        expected = {
+            'P': {'picker': 'auto', 'snrdb': 15.405649120980094, 'network': u'GR', 'weight': 0, 'Ao': None, 'Mo': None,
+                  'marked': [], 'lpp': UTCDateTime(2016, 1, 24, 10, 41, 32, 690000), 'Mw': None, 'fc': None,
+                  'snr': 34.718816470730317, 'epp': UTCDateTime(2016, 1, 24, 10, 41, 28, 890000),
+                  'mpp': UTCDateTime(2016, 1, 24, 10, 41, 31, 690000), 'w0': None, 'spe': 0.9333333333333323, 'fm': 'D',
+                  'channel': u'LHZ'},
+            'S': {'picker': 'auto', 'snrdb': None, 'network': None, 'weight': 4, 'Mo': None, 'Ao': None, 'lpp': None,
+                  'Mw': None, 'fc': None, 'snr': None, 'marked': [], 'mpp': None, 'w0': None, 'spe': None, 'epp': None,
+                  'fm': 'N', 'channel': None}}
         with HidePrints():
-            result, station = autopickstation(wfstream=wfstream, pickparam=self.pickparam_taupy_disabled, metadata=(None, None))
+            result, station = autopickstation(wfstream=wfstream, pickparam=self.pickparam_taupy_disabled,
+                                              metadata=(None, None))
         self.assertEqual(expected, result)
         self.assertEqual('GRA1', station)
 
     def test_autopickstation_a106_taupy_enabled(self):
         """This station has invalid values recorded on both N and E component, but a pick can still be found on Z"""
-        expected = {'P': {'picker': 'auto', 'snrdb': 12.862128789922826, 'network': u'Z3', 'weight': 0, 'Ao': None, 'Mo': None, 'marked': [], 'lpp': UTCDateTime(2016, 1, 24, 10, 41, 34), 'Mw': None, 'fc': None, 'snr': 19.329155459132608, 'epp': UTCDateTime(2016, 1, 24, 10, 41, 30), 'mpp': UTCDateTime(2016, 1, 24, 10, 41, 33), 'w0': None, 'spe': 1.6666666666666667, 'fm': None, 'channel': u'LHZ'}, 'S': {'picker': 'auto', 'snrdb': None, 'network': u'Z3', 'weight': 4, 'Ao': None, 'Mo': None, 'marked': [], 'lpp': UTCDateTime(2016, 1, 24, 10, 28, 56), 'Mw': None, 'fc': None, 'snr': None, 'epp': UTCDateTime(2016, 1, 24, 10, 28, 24), 'mpp': UTCDateTime(2016, 1, 24, 10, 28, 40), 'w0': None, 'spe': None, 'fm': None, 'channel': u'LHE'}}
+        expected = {
+            'P': {'picker': 'auto', 'snrdb': 12.862128789922826, 'network': u'Z3', 'weight': 0, 'Ao': None, 'Mo': None,
+                  'marked': [], 'lpp': UTCDateTime(2016, 1, 24, 10, 41, 34), 'Mw': None, 'fc': None,
+                  'snr': 19.329155459132608, 'epp': UTCDateTime(2016, 1, 24, 10, 41, 30),
+                  'mpp': UTCDateTime(2016, 1, 24, 10, 41, 33), 'w0': None, 'spe': 1.6666666666666667, 'fm': None,
+                  'channel': u'LHZ'},
+            'S': {'picker': 'auto', 'snrdb': None, 'network': u'Z3', 'weight': 4, 'Ao': None, 'Mo': None, 'marked': [],
+                  'lpp': UTCDateTime(2016, 1, 24, 10, 28, 56), 'Mw': None, 'fc': None, 'snr': None,
+                  'epp': UTCDateTime(2016, 1, 24, 10, 28, 24), 'mpp': UTCDateTime(2016, 1, 24, 10, 28, 40), 'w0': None,
+                  'spe': None, 'fm': None, 'channel': u'LHE'}}
         with HidePrints():
-            result, station = autopickstation(wfstream=self.a106, pickparam=self.pickparam_taupy_enabled, metadata=self.metadata, origin=self.origin)
+            result, station = autopickstation(wfstream=self.a106, pickparam=self.pickparam_taupy_enabled,
+                                              metadata=self.metadata, origin=self.origin)
         self.assertEqual(expected, result)
 
     def test_autopickstation_station_missing_in_metadata(self):
@@ -202,10 +297,22 @@ class TestAutopickStation(unittest.TestCase):
         relative to the theoretical onset to one relative to the traces starttime, eg never negative.
         """
         self.pickparam_taupy_enabled.setParamKV('pstart', -100)  # modify starttime to be relative to theoretical onset
-        expected = {'P': {'picker': 'auto', 'snrdb': 14.464757855513506, 'network': u'Z3', 'weight': 0, 'Mo': None, 'Ao': None, 'lpp': UTCDateTime(2016, 1, 24, 10, 41, 39, 605000), 'Mw': None, 'fc': None, 'snr': 27.956048519707181, 'marked': [], 'mpp': UTCDateTime(2016, 1, 24, 10, 41, 38, 605000), 'w0': None, 'spe': 1.6666666666666667, 'epp': UTCDateTime(2016, 1, 24, 10, 41, 35, 605000), 'fm': None, 'channel': u'LHZ'}, 'S': {'picker': 'auto', 'snrdb': 10.112844176301248, 'network': u'Z3', 'weight': 1, 'Mo': None, 'Ao': None, 'lpp': UTCDateTime(2016, 1, 24, 10, 50, 51, 605000), 'Mw': None, 'fc': None, 'snr': 10.263238413785425, 'marked': [], 'mpp': UTCDateTime(2016, 1, 24, 10, 50, 48, 605000), 'w0': None, 'spe': 4.666666666666667, 'epp': UTCDateTime(2016, 1, 24, 10, 50, 40, 605000), 'fm': None, 'channel': u'LHE'}}
+        expected = {
+            'P': {'picker': 'auto', 'snrdb': 14.464757855513506, 'network': u'Z3', 'weight': 0, 'Mo': None, 'Ao': None,
+                  'lpp': UTCDateTime(2016, 1, 24, 10, 41, 39, 605000), 'Mw': None, 'fc': None,
+                  'snr': 27.956048519707181, 'marked': [], 'mpp': UTCDateTime(2016, 1, 24, 10, 41, 38, 605000),
+                  'w0': None, 'spe': 1.6666666666666667, 'epp': UTCDateTime(2016, 1, 24, 10, 41, 35, 605000),
+                  'fm': None, 'channel': u'LHZ'},
+            'S': {'picker': 'auto', 'snrdb': 10.112844176301248, 'network': u'Z3', 'weight': 1, 'Mo': None, 'Ao': None,
+                  'lpp': UTCDateTime(2016, 1, 24, 10, 50, 51, 605000), 'Mw': None, 'fc': None,
+                  'snr': 10.263238413785425, 'marked': [], 'mpp': UTCDateTime(2016, 1, 24, 10, 50, 48, 605000),
+                  'w0': None, 'spe': 4.666666666666667, 'epp': UTCDateTime(2016, 1, 24, 10, 50, 40, 605000), 'fm': None,
+                  'channel': u'LHE'}}
         with HidePrints():
-            result, station = autopickstation(wfstream = self.a005a, pickparam=self.pickparam_taupy_enabled, metadata=self.metadata, origin=self.origin)
+            result, station = autopickstation(wfstream=self.a005a, pickparam=self.pickparam_taupy_enabled,
+                                              metadata=self.metadata, origin=self.origin)
         self.assertEqual(expected, result)
 
+
 if __name__ == '__main__':
     unittest.main()

tests/test_get_qualities_from_xml.py

@@ -0,0 +1,33 @@
+import unittest
+
+from pylot.core.io.phases import getQualitiesfromxml
+
+
+class TestQualityFromXML(unittest.TestCase):
+    def setUp(self):
+        self.path = '.'
+        self.ErrorsP = [0.02, 0.04, 0.08, 0.16]
+        self.ErrorsS = [0.04, 0.08, 0.16, 0.32]
+        self.test0_result = [[0.0136956521739, 0.0126, 0.0101612903226, 0.00734848484849, 0.0135069444444,
+                              0.00649659863946, 0.0129513888889, 0.0122747747748, 0.0119252873563, 0.0103947368421,
+                              0.0092380952381, 0.00916666666667, 0.0104444444444, 0.0125333333333, 0.00904761904762,
+                              0.00885714285714, 0.00911616161616, 0.0164166666667, 0.0128787878788, 0.0122756410256,
+                              0.013653253667966917], [0.0239333333333, 0.0223791578953, 0.0217974304255],
+                             [0.0504861111111, 0.0610833333333], [], [0.171029411765]], [
+                                [0.0195, 0.0203623188406, 0.0212121212121, 0.0345833333333, 0.0196180555556,
+                                 0.0202536231884, 0.0200347222222, 0.0189, 0.0210763888889, 0.018275862069,
+                                 0.0213888888889, 0.0319791666667, 0.0205303030303, 0.0156388888889, 0.0192,
+                                 0.0231349206349, 0.023625, 0.02875, 0.0195512820513, 0.0239393939394, 0.0234166666667,
+                                 0.0174702380952, 0.0204151307995], [0.040314343081226646], [0.148555555556], [], []]
+        self.test1_result = [77.77777777777777, 11.11111111111111, 7.407407407407407, 0, 3.7037037037037037],\
+                            [92.0, 4.0, 4.0, 0, 0]
+
+    def test_result_plotflag0(self):
+        self.assertEqual(getQualitiesfromxml(self.path, self.ErrorsP, self.ErrorsS, 0), self.test0_result)
+
+    def test_result_plotflag1(self):
+        self.assertEqual(getQualitiesfromxml(self.path, self.ErrorsP, self.ErrorsS, 1), self.test1_result)
+
+
+if __name__ == '__main__':
+    unittest.main()

tests/test_get_quality_class.py

@@ -1,4 +1,5 @@
 import unittest
+
 from pylot.core.pick.utils import get_quality_class
 
 
@@ -52,5 +53,6 @@ class TestQualityClassFromUncertainty(unittest.TestCase):
         # Error exactly in class 3
         self.assertEqual(3, get_quality_class(5.6, self.error_classes))
 
+
 if __name__ == '__main__':
     unittest.main()

tests/utils.py

@@ -33,6 +33,7 @@ class HidePrints:
         def silencer(*args, **kwargs):
             with HidePrints():
                 func(*args, **kwargs)
+
         return silencer
 
     def __init__(self, hide_prints=True):